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author | Xusen Yin <yinxusen@gmail.com> | 2015-11-17 23:44:06 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-11-17 23:44:06 -0800 |
commit | 9154f89befb7a33d4853cea95efd7dc6b25d033b (patch) | |
tree | 8eb6da0ff09ba6c3b2fe34859077e5a55c5ed3df | |
parent | 2f191c66b668fc97f82f44fd8336b6a4488c2f5d (diff) | |
download | spark-9154f89befb7a33d4853cea95efd7dc6b25d033b.tar.gz spark-9154f89befb7a33d4853cea95efd7dc6b25d033b.tar.bz2 spark-9154f89befb7a33d4853cea95efd7dc6b25d033b.zip |
[SPARK-11728] Replace example code in ml-ensembles.md using include_example
JIRA issue https://issues.apache.org/jira/browse/SPARK-11728.
The ml-ensembles.md file contains `OneVsRestExample`. Instead of writing new code files of two `OneVsRestExample`s, I use two existing files in the examples directory, they are `OneVsRestExample.scala` and `JavaOneVsRestExample.scala`.
Author: Xusen Yin <yinxusen@gmail.com>
Closes #9716 from yinxusen/SPARK-11728.
15 files changed, 1070 insertions, 740 deletions
diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md index ce15f5e646..f6c3c30d53 100644 --- a/docs/ml-ensembles.md +++ b/docs/ml-ensembles.md @@ -115,194 +115,21 @@ We use two feature transformers to prepare the data; these help index categories Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.classification.RandomForestClassifier -import org.apache.spark.ml.classification.RandomForestClassificationModel -import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer} -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// Index labels, adding metadata to the label column. -// Fit on whole dataset to include all labels in index. -val labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data) -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -val featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data) - -// Split the data into training and test sets (30% held out for testing) -val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) - -// Train a RandomForest model. -val rf = new RandomForestClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setNumTrees(10) - -// Convert indexed labels back to original labels. -val labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels) - -// Chain indexers and forest in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter)) - -// Train model. This also runs the indexers. -val model = pipeline.fit(trainingData) - -// Make predictions. -val predictions = model.transform(testData) - -// Select example rows to display. -predictions.select("predictedLabel", "label", "features").show(5) - -// Select (prediction, true label) and compute test error -val evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("precision") -val accuracy = evaluator.evaluate(predictions) -println("Test Error = " + (1.0 - accuracy)) - -val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] -println("Learned classification forest model:\n" + rfModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/RandomForestClassifier.html) for more details. -{% highlight java %} -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.RandomForestClassifier; -import org.apache.spark.ml.classification.RandomForestClassificationModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - -// Index labels, adding metadata to the label column. -// Fit on whole dataset to include all labels in index. -StringIndexerModel labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data); -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - -// Split the data into training and test sets (30% held out for testing) -DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); -DataFrame trainingData = splits[0]; -DataFrame testData = splits[1]; - -// Train a RandomForest model. -RandomForestClassifier rf = new RandomForestClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures"); - -// Convert indexed labels back to original labels. -IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - -// Chain indexers and forest in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter}); - -// Train model. This also runs the indexers. -PipelineModel model = pipeline.fit(trainingData); - -// Make predictions. -DataFrame predictions = model.transform(testData); - -// Select example rows to display. -predictions.select("predictedLabel", "label", "features").show(5); - -// Select (prediction, true label) and compute test error -MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("precision"); -double accuracy = evaluator.evaluate(predictions); -System.out.println("Test Error = " + (1.0 - accuracy)); - -RandomForestClassificationModel rfModel = - (RandomForestClassificationModel)(model.stages()[2]); -System.out.println("Learned classification forest model:\n" + rfModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.classification import RandomForestClassifier -from pyspark.ml.feature import StringIndexer, VectorIndexer -from pyspark.ml.evaluation import MulticlassClassificationEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# Index labels, adding metadata to the label column. -# Fit on whole dataset to include all labels in index. -labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) -# Automatically identify categorical features, and index them. -# Set maxCategories so features with > 4 distinct values are treated as continuous. -featureIndexer =\ - VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) - -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a RandomForest model. -rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") - -# Chain indexers and forest in a Pipeline -pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf]) - -# Train model. This also runs the indexers. -model = pipeline.fit(trainingData) - -# Make predictions. -predictions = model.transform(testData) - -# Select example rows to display. -predictions.select("prediction", "indexedLabel", "features").show(5) - -# Select (prediction, true label) and compute test error -evaluator = MulticlassClassificationEvaluator( - labelCol="indexedLabel", predictionCol="prediction", metricName="precision") -accuracy = evaluator.evaluate(predictions) -print "Test Error = %g" % (1.0 - accuracy) - -rfModel = model.stages[2] -print rfModel # summary only -{% endhighlight %} +{% include_example python/ml/random_forest_classifier_example.py %} </div> </div> @@ -316,167 +143,21 @@ We use a feature transformer to index categorical features, adding metadata to t Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.regression.RandomForestRegressor -import org.apache.spark.ml.regression.RandomForestRegressionModel -import org.apache.spark.ml.feature.VectorIndexer -import org.apache.spark.ml.evaluation.RegressionEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -val featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data) - -// Split the data into training and test sets (30% held out for testing) -val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) - -// Train a RandomForest model. -val rf = new RandomForestRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - -// Chain indexer and forest in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(featureIndexer, rf)) - -// Train model. This also runs the indexer. -val model = pipeline.fit(trainingData) - -// Make predictions. -val predictions = model.transform(testData) - -// Select example rows to display. -predictions.select("prediction", "label", "features").show(5) - -// Select (prediction, true label) and compute test error -val evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse") -val rmse = evaluator.evaluate(predictions) -println("Root Mean Squared Error (RMSE) on test data = " + rmse) - -val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel] -println("Learned regression forest model:\n" + rfModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/RandomForestRegressor.html) for more details. -{% highlight java %} -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.ml.regression.RandomForestRegressionModel; -import org.apache.spark.ml.regression.RandomForestRegressor; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - -// Split the data into training and test sets (30% held out for testing) -DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); -DataFrame trainingData = splits[0]; -DataFrame testData = splits[1]; - -// Train a RandomForest model. -RandomForestRegressor rf = new RandomForestRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures"); - -// Chain indexer and forest in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {featureIndexer, rf}); - -// Train model. This also runs the indexer. -PipelineModel model = pipeline.fit(trainingData); - -// Make predictions. -DataFrame predictions = model.transform(testData); - -// Select example rows to display. -predictions.select("prediction", "label", "features").show(5); - -// Select (prediction, true label) and compute test error -RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse"); -double rmse = evaluator.evaluate(predictions); -System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); - -RandomForestRegressionModel rfModel = - (RandomForestRegressionModel)(model.stages()[1]); -System.out.println("Learned regression forest model:\n" + rfModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.regression import RandomForestRegressor -from pyspark.ml.feature import VectorIndexer -from pyspark.ml.evaluation import RegressionEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# Automatically identify categorical features, and index them. -# Set maxCategories so features with > 4 distinct values are treated as continuous. -featureIndexer =\ - VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) - -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a RandomForest model. -rf = RandomForestRegressor(featuresCol="indexedFeatures") - -# Chain indexer and forest in a Pipeline -pipeline = Pipeline(stages=[featureIndexer, rf]) - -# Train model. This also runs the indexer. -model = pipeline.fit(trainingData) - -# Make predictions. -predictions = model.transform(testData) - -# Select example rows to display. -predictions.select("prediction", "label", "features").show(5) - -# Select (prediction, true label) and compute test error -evaluator = RegressionEvaluator( - labelCol="label", predictionCol="prediction", metricName="rmse") -rmse = evaluator.evaluate(predictions) -print "Root Mean Squared Error (RMSE) on test data = %g" % rmse - -rfModel = model.stages[1] -print rfModel # summary only -{% endhighlight %} +{% include_example python/ml/random_forest_regressor_example.py %} </div> </div> @@ -560,194 +241,21 @@ We use two feature transformers to prepare the data; these help index categories Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.classification.GBTClassifier -import org.apache.spark.ml.classification.GBTClassificationModel -import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer} -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// Index labels, adding metadata to the label column. -// Fit on whole dataset to include all labels in index. -val labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data) -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -val featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data) - -// Split the data into training and test sets (30% held out for testing) -val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) - -// Train a GBT model. -val gbt = new GBTClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10) - -// Convert indexed labels back to original labels. -val labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels) - -// Chain indexers and GBT in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter)) - -// Train model. This also runs the indexers. -val model = pipeline.fit(trainingData) - -// Make predictions. -val predictions = model.transform(testData) - -// Select example rows to display. -predictions.select("predictedLabel", "label", "features").show(5) - -// Select (prediction, true label) and compute test error -val evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("precision") -val accuracy = evaluator.evaluate(predictions) -println("Test Error = " + (1.0 - accuracy)) - -val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel] -println("Learned classification GBT model:\n" + gbtModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/GBTClassifier.html) for more details. -{% highlight java %} -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.GBTClassifier; -import org.apache.spark.ml.classification.GBTClassificationModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - -// Index labels, adding metadata to the label column. -// Fit on whole dataset to include all labels in index. -StringIndexerModel labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data); -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - -// Split the data into training and test sets (30% held out for testing) -DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); -DataFrame trainingData = splits[0]; -DataFrame testData = splits[1]; - -// Train a GBT model. -GBTClassifier gbt = new GBTClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10); - -// Convert indexed labels back to original labels. -IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - -// Chain indexers and GBT in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter}); - -// Train model. This also runs the indexers. -PipelineModel model = pipeline.fit(trainingData); - -// Make predictions. -DataFrame predictions = model.transform(testData); - -// Select example rows to display. -predictions.select("predictedLabel", "label", "features").show(5); - -// Select (prediction, true label) and compute test error -MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("precision"); -double accuracy = evaluator.evaluate(predictions); -System.out.println("Test Error = " + (1.0 - accuracy)); - -GBTClassificationModel gbtModel = - (GBTClassificationModel)(model.stages()[2]); -System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.classification import GBTClassifier -from pyspark.ml.feature import StringIndexer, VectorIndexer -from pyspark.ml.evaluation import MulticlassClassificationEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# Index labels, adding metadata to the label column. -# Fit on whole dataset to include all labels in index. -labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) -# Automatically identify categorical features, and index them. -# Set maxCategories so features with > 4 distinct values are treated as continuous. -featureIndexer =\ - VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) - -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a GBT model. -gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10) - -# Chain indexers and GBT in a Pipeline -pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt]) - -# Train model. This also runs the indexers. -model = pipeline.fit(trainingData) - -# Make predictions. -predictions = model.transform(testData) - -# Select example rows to display. -predictions.select("prediction", "indexedLabel", "features").show(5) - -# Select (prediction, true label) and compute test error -evaluator = MulticlassClassificationEvaluator( - labelCol="indexedLabel", predictionCol="prediction", metricName="precision") -accuracy = evaluator.evaluate(predictions) -print "Test Error = %g" % (1.0 - accuracy) - -gbtModel = model.stages[2] -print gbtModel # summary only -{% endhighlight %} +{% include_example python/ml/gradient_boosted_tree_classifier_example.py %} </div> </div> @@ -761,168 +269,21 @@ be true in general. Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.regression.GBTRegressor -import org.apache.spark.ml.regression.GBTRegressionModel -import org.apache.spark.ml.feature.VectorIndexer -import org.apache.spark.ml.evaluation.RegressionEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -val featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data) - -// Split the data into training and test sets (30% held out for testing) -val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) - -// Train a GBT model. -val gbt = new GBTRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10) - -// Chain indexer and GBT in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(featureIndexer, gbt)) - -// Train model. This also runs the indexer. -val model = pipeline.fit(trainingData) - -// Make predictions. -val predictions = model.transform(testData) - -// Select example rows to display. -predictions.select("prediction", "label", "features").show(5) - -// Select (prediction, true label) and compute test error -val evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse") -val rmse = evaluator.evaluate(predictions) -println("Root Mean Squared Error (RMSE) on test data = " + rmse) - -val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel] -println("Learned regression GBT model:\n" + gbtModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/GBTRegressor.html) for more details. -{% highlight java %} -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.ml.regression.GBTRegressionModel; -import org.apache.spark.ml.regression.GBTRegressor; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - -// Split the data into training and test sets (30% held out for testing) -DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); -DataFrame trainingData = splits[0]; -DataFrame testData = splits[1]; - -// Train a GBT model. -GBTRegressor gbt = new GBTRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10); - -// Chain indexer and GBT in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {featureIndexer, gbt}); - -// Train model. This also runs the indexer. -PipelineModel model = pipeline.fit(trainingData); - -// Make predictions. -DataFrame predictions = model.transform(testData); - -// Select example rows to display. -predictions.select("prediction", "label", "features").show(5); - -// Select (prediction, true label) and compute test error -RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse"); -double rmse = evaluator.evaluate(predictions); -System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); - -GBTRegressionModel gbtModel = - (GBTRegressionModel)(model.stages()[1]); -System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.regression import GBTRegressor -from pyspark.ml.feature import VectorIndexer -from pyspark.ml.evaluation import RegressionEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# Automatically identify categorical features, and index them. -# Set maxCategories so features with > 4 distinct values are treated as continuous. -featureIndexer =\ - VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) - -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a GBT model. -gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10) - -# Chain indexer and GBT in a Pipeline -pipeline = Pipeline(stages=[featureIndexer, gbt]) - -# Train model. This also runs the indexer. -model = pipeline.fit(trainingData) - -# Make predictions. -predictions = model.transform(testData) - -# Select example rows to display. -predictions.select("prediction", "label", "features").show(5) - -# Select (prediction, true label) and compute test error -evaluator = RegressionEvaluator( - labelCol="label", predictionCol="prediction", metricName="rmse") -rmse = evaluator.evaluate(predictions) -print "Root Mean Squared Error (RMSE) on test data = %g" % rmse - -gbtModel = model.stages[1] -print gbtModel # summary only -{% endhighlight %} +{% include_example python/ml/gradient_boosted_tree_regressor_example.py %} </div> </div> @@ -945,100 +306,13 @@ The example below demonstrates how to load the Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) for more details. -{% highlight scala %} -import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest} -import org.apache.spark.mllib.evaluation.MulticlassMetrics -import org.apache.spark.sql.{Row, SQLContext} - -val sqlContext = new SQLContext(sc) - -// parse data into dataframe -val data = sqlContext.read.format("libsvm") - .load("data/mllib/sample_multiclass_classification_data.txt") -val Array(train, test) = data.randomSplit(Array(0.7, 0.3)) - -// instantiate multiclass learner and train -val ovr = new OneVsRest().setClassifier(new LogisticRegression) - -val ovrModel = ovr.fit(train) - -// score model on test data -val predictions = ovrModel.transform(test).select("prediction", "label") -val predictionsAndLabels = predictions.map {case Row(p: Double, l: Double) => (p, l)} - -// compute confusion matrix -val metrics = new MulticlassMetrics(predictionsAndLabels) -println(metrics.confusionMatrix) - -// the Iris DataSet has three classes -val numClasses = 3 - -println("label\tfpr\n") -(0 until numClasses).foreach { index => - val label = index.toDouble - println(label + "\t" + metrics.falsePositiveRate(label)) -} -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/OneVsRestExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRest.html) for more details. -{% highlight java %} -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.OneVsRest; -import org.apache.spark.ml.classification.OneVsRestModel; -import org.apache.spark.mllib.evaluation.MulticlassMetrics; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.SQLContext; - -SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample"); -JavaSparkContext jsc = new JavaSparkContext(conf); -SQLContext jsql = new SQLContext(jsc); - -DataFrame dataFrame = sqlContext.read().format("libsvm") - .load("data/mllib/sample_multiclass_classification_data.txt"); - -DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345); -DataFrame train = splits[0]; -DataFrame test = splits[1]; - -// instantiate the One Vs Rest Classifier -OneVsRest ovr = new OneVsRest().setClassifier(new LogisticRegression()); - -// train the multiclass model -OneVsRestModel ovrModel = ovr.fit(train.cache()); - -// score the model on test data -DataFrame predictions = ovrModel - .transform(test) - .select("prediction", "label"); - -// obtain metrics -MulticlassMetrics metrics = new MulticlassMetrics(predictions); -Matrix confusionMatrix = metrics.confusionMatrix(); - -// output the Confusion Matrix -System.out.println("Confusion Matrix"); -System.out.println(confusionMatrix); - -// compute the false positive rate per label -System.out.println(); -System.out.println("label\tfpr\n"); - -// the Iris DataSet has three classes -int numClasses = 3; -for (int index = 0; index < numClasses; index++) { - double label = (double) index; - System.out.print(label); - System.out.print("\t"); - System.out.print(metrics.falsePositiveRate(label)); - System.out.println(); -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaOneVsRestExample.java %} </div> </div> diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java new file mode 100644 index 0000000000..848fe6566c --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java @@ -0,0 +1,102 @@ +/* + * 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.ml; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +// $example on$ +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.classification.GBTClassificationModel; +import org.apache.spark.ml.classification.GBTClassifier; +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; +import org.apache.spark.ml.feature.*; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +// $example off$ + +public class JavaGradientBoostedTreeClassifierExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaGradientBoostedTreeClassifierExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); + + // Index labels, adding metadata to the label column. + // Fit on whole dataset to include all labels in index. + StringIndexerModel labelIndexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("indexedLabel") + .fit(data); + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + VectorIndexerModel featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data); + + // Split the data into training and test sets (30% held out for testing) + DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); + DataFrame trainingData = splits[0]; + DataFrame testData = splits[1]; + + // Train a GBT model. + GBTClassifier gbt = new GBTClassifier() + .setLabelCol("indexedLabel") + .setFeaturesCol("indexedFeatures") + .setMaxIter(10); + + // Convert indexed labels back to original labels. + IndexToString labelConverter = new IndexToString() + .setInputCol("prediction") + .setOutputCol("predictedLabel") + .setLabels(labelIndexer.labels()); + + // Chain indexers and GBT in a Pipeline + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter}); + + // Train model. This also runs the indexers. + PipelineModel model = pipeline.fit(trainingData); + + // Make predictions. + DataFrame predictions = model.transform(testData); + + // Select example rows to display. + predictions.select("predictedLabel", "label", "features").show(5); + + // Select (prediction, true label) and compute test error + MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("indexedLabel") + .setPredictionCol("prediction") + .setMetricName("precision"); + double accuracy = evaluator.evaluate(predictions); + System.out.println("Test Error = " + (1.0 - accuracy)); + + GBTClassificationModel gbtModel = (GBTClassificationModel)(model.stages()[2]); + System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString()); + // $example off$ + + jsc.stop(); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java new file mode 100644 index 0000000000..1f67b0842d --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java @@ -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. + */ + +package org.apache.spark.examples.ml; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +// $example on$ +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.evaluation.RegressionEvaluator; +import org.apache.spark.ml.feature.VectorIndexer; +import org.apache.spark.ml.feature.VectorIndexerModel; +import org.apache.spark.ml.regression.GBTRegressionModel; +import org.apache.spark.ml.regression.GBTRegressor; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +// $example off$ + +public class JavaGradientBoostedTreeRegressorExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaGradientBoostedTreeRegressorExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); + + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + VectorIndexerModel featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data); + + // Split the data into training and test sets (30% held out for testing) + DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); + DataFrame trainingData = splits[0]; + DataFrame testData = splits[1]; + + // Train a GBT model. + GBTRegressor gbt = new GBTRegressor() + .setLabelCol("label") + .setFeaturesCol("indexedFeatures") + .setMaxIter(10); + + // Chain indexer and GBT in a Pipeline + Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {featureIndexer, gbt}); + + // Train model. This also runs the indexer. + PipelineModel model = pipeline.fit(trainingData); + + // Make predictions. + DataFrame predictions = model.transform(testData); + + // Select example rows to display. + predictions.select("prediction", "label", "features").show(5); + + // Select (prediction, true label) and compute test error + RegressionEvaluator evaluator = new RegressionEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("rmse"); + double rmse = evaluator.evaluate(predictions); + System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); + + GBTRegressionModel gbtModel = (GBTRegressionModel)(model.stages()[1]); + System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString()); + // $example off$ + + jsc.stop(); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java index f0d92a56be..42374e77ac 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java @@ -21,6 +21,7 @@ import org.apache.commons.cli.*; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaSparkContext; +// $example on$ import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.OneVsRest; import org.apache.spark.ml.classification.OneVsRestModel; @@ -31,6 +32,7 @@ import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.types.StructField; +// $example off$ /** * An example runner for Multiclass to Binary Reduction with One Vs Rest. @@ -61,6 +63,7 @@ public class JavaOneVsRestExample { JavaSparkContext jsc = new JavaSparkContext(conf); SQLContext jsql = new SQLContext(jsc); + // $example on$ // configure the base classifier LogisticRegression classifier = new LogisticRegression() .setMaxIter(params.maxIter) @@ -125,6 +128,7 @@ public class JavaOneVsRestExample { System.out.println(confusionMatrix); System.out.println(); System.out.println(results); + // $example off$ jsc.stop(); } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java new file mode 100644 index 0000000000..5a62496660 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java @@ -0,0 +1,101 @@ +/* + * 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.ml; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +// $example on$ +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.classification.RandomForestClassificationModel; +import org.apache.spark.ml.classification.RandomForestClassifier; +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; +import org.apache.spark.ml.feature.*; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +// $example off$ + +public class JavaRandomForestClassifierExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaRandomForestClassifierExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); + + // Index labels, adding metadata to the label column. + // Fit on whole dataset to include all labels in index. + StringIndexerModel labelIndexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("indexedLabel") + .fit(data); + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + VectorIndexerModel featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data); + + // Split the data into training and test sets (30% held out for testing) + DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); + DataFrame trainingData = splits[0]; + DataFrame testData = splits[1]; + + // Train a RandomForest model. + RandomForestClassifier rf = new RandomForestClassifier() + .setLabelCol("indexedLabel") + .setFeaturesCol("indexedFeatures"); + + // Convert indexed labels back to original labels. + IndexToString labelConverter = new IndexToString() + .setInputCol("prediction") + .setOutputCol("predictedLabel") + .setLabels(labelIndexer.labels()); + + // Chain indexers and forest in a Pipeline + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter}); + + // Train model. This also runs the indexers. + PipelineModel model = pipeline.fit(trainingData); + + // Make predictions. + DataFrame predictions = model.transform(testData); + + // Select example rows to display. + predictions.select("predictedLabel", "label", "features").show(5); + + // Select (prediction, true label) and compute test error + MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("indexedLabel") + .setPredictionCol("prediction") + .setMetricName("precision"); + double accuracy = evaluator.evaluate(predictions); + System.out.println("Test Error = " + (1.0 - accuracy)); + + RandomForestClassificationModel rfModel = (RandomForestClassificationModel)(model.stages()[2]); + System.out.println("Learned classification forest model:\n" + rfModel.toDebugString()); + // $example off$ + + jsc.stop(); + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java new file mode 100644 index 0000000000..05782a0724 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java @@ -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. + */ + +package org.apache.spark.examples.ml; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +// $example on$ +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.evaluation.RegressionEvaluator; +import org.apache.spark.ml.feature.VectorIndexer; +import org.apache.spark.ml.feature.VectorIndexerModel; +import org.apache.spark.ml.regression.RandomForestRegressionModel; +import org.apache.spark.ml.regression.RandomForestRegressor; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +// $example off$ + +public class JavaRandomForestRegressorExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaRandomForestRegressorExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); + + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + VectorIndexerModel featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data); + + // Split the data into training and test sets (30% held out for testing) + DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); + DataFrame trainingData = splits[0]; + DataFrame testData = splits[1]; + + // Train a RandomForest model. + RandomForestRegressor rf = new RandomForestRegressor() + .setLabelCol("label") + .setFeaturesCol("indexedFeatures"); + + // Chain indexer and forest in a Pipeline + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[] {featureIndexer, rf}); + + // Train model. This also runs the indexer. + PipelineModel model = pipeline.fit(trainingData); + + // Make predictions. + DataFrame predictions = model.transform(testData); + + // Select example rows to display. + predictions.select("prediction", "label", "features").show(5); + + // Select (prediction, true label) and compute test error + RegressionEvaluator evaluator = new RegressionEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("rmse"); + double rmse = evaluator.evaluate(predictions); + System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); + + RandomForestRegressionModel rfModel = (RandomForestRegressionModel)(model.stages()[1]); + System.out.println("Learned regression forest model:\n" + rfModel.toDebugString()); + // $example off$ + + jsc.stop(); + } +} diff --git a/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py b/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py new file mode 100644 index 0000000000..028497651f --- /dev/null +++ b/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py @@ -0,0 +1,77 @@ +# +# 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. +# + +""" +Gradient Boosted Tree Classifier Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext, SQLContext +# $example on$ +from pyspark.ml import Pipeline +from pyspark.ml.classification import GBTClassifier +from pyspark.ml.feature import StringIndexer, VectorIndexer +from pyspark.ml.evaluation import MulticlassClassificationEvaluator +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="gradient_boosted_tree_classifier_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + # Index labels, adding metadata to the label column. + # Fit on whole dataset to include all labels in index. + labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) + # Automatically identify categorical features, and index them. + # Set maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a GBT model. + gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10) + + # Chain indexers and GBT in a Pipeline + pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt]) + + # Train model. This also runs the indexers. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "indexedLabel", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = MulticlassClassificationEvaluator( + labelCol="indexedLabel", predictionCol="prediction", metricName="precision") + accuracy = evaluator.evaluate(predictions) + print("Test Error = %g" % (1.0 - accuracy)) + + gbtModel = model.stages[2] + print(gbtModel) # summary only + # $example off$ + + sc.stop() diff --git a/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py b/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py new file mode 100644 index 0000000000..4246e133a9 --- /dev/null +++ b/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py @@ -0,0 +1,74 @@ +# +# 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. +# + +""" +Gradient Boosted Tree Regressor Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext, SQLContext +# $example on$ +from pyspark.ml import Pipeline +from pyspark.ml.regression import GBTRegressor +from pyspark.ml.feature import VectorIndexer +from pyspark.ml.evaluation import RegressionEvaluator +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="gradient_boosted_tree_regressor_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + # Automatically identify categorical features, and index them. + # Set maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a GBT model. + gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10) + + # Chain indexer and GBT in a Pipeline + pipeline = Pipeline(stages=[featureIndexer, gbt]) + + # Train model. This also runs the indexer. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "label", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = RegressionEvaluator( + labelCol="label", predictionCol="prediction", metricName="rmse") + rmse = evaluator.evaluate(predictions) + print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) + + gbtModel = model.stages[1] + print(gbtModel) # summary only + # $example off$ + + sc.stop() diff --git a/examples/src/main/python/ml/random_forest_classifier_example.py b/examples/src/main/python/ml/random_forest_classifier_example.py new file mode 100644 index 0000000000..b3530d4f41 --- /dev/null +++ b/examples/src/main/python/ml/random_forest_classifier_example.py @@ -0,0 +1,77 @@ +# +# 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. +# + +""" +Random Forest Classifier Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext, SQLContext +# $example on$ +from pyspark.ml import Pipeline +from pyspark.ml.classification import RandomForestClassifier +from pyspark.ml.feature import StringIndexer, VectorIndexer +from pyspark.ml.evaluation import MulticlassClassificationEvaluator +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="random_forest_classifier_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + # Index labels, adding metadata to the label column. + # Fit on whole dataset to include all labels in index. + labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) + # Automatically identify categorical features, and index them. + # Set maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a RandomForest model. + rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") + + # Chain indexers and forest in a Pipeline + pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf]) + + # Train model. This also runs the indexers. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "indexedLabel", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = MulticlassClassificationEvaluator( + labelCol="indexedLabel", predictionCol="prediction", metricName="precision") + accuracy = evaluator.evaluate(predictions) + print("Test Error = %g" % (1.0 - accuracy)) + + rfModel = model.stages[2] + print(rfModel) # summary only + # $example off$ + + sc.stop() diff --git a/examples/src/main/python/ml/random_forest_regressor_example.py b/examples/src/main/python/ml/random_forest_regressor_example.py new file mode 100644 index 0000000000..b59c7c9414 --- /dev/null +++ b/examples/src/main/python/ml/random_forest_regressor_example.py @@ -0,0 +1,74 @@ +# +# 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. +# + +""" +Random Forest Regressor Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext, SQLContext +# $example on$ +from pyspark.ml import Pipeline +from pyspark.ml.regression import RandomForestRegressor +from pyspark.ml.feature import VectorIndexer +from pyspark.ml.evaluation import RegressionEvaluator +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="random_forest_regressor_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + # Automatically identify categorical features, and index them. + # Set maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a RandomForest model. + rf = RandomForestRegressor(featuresCol="indexedFeatures") + + # Chain indexer and forest in a Pipeline + pipeline = Pipeline(stages=[featureIndexer, rf]) + + # Train model. This also runs the indexer. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "label", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = RegressionEvaluator( + labelCol="label", predictionCol="prediction", metricName="rmse") + rmse = evaluator.evaluate(predictions) + print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) + + rfModel = model.stages[1] + print(rfModel) # summary only + # $example off$ + + sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala new file mode 100644 index 0000000000..474af7db4b --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala @@ -0,0 +1,97 @@ +/* + * 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.ml + +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkConf, SparkContext} +// $example on$ +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier} +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator +import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer} +// $example off$ + +object GradientBoostedTreeClassifierExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("GradientBoostedTreeClassifierExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + // Index labels, adding metadata to the label column. + // Fit on whole dataset to include all labels in index. + val labelIndexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("indexedLabel") + .fit(data) + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + val featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data) + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a GBT model. + val gbt = new GBTClassifier() + .setLabelCol("indexedLabel") + .setFeaturesCol("indexedFeatures") + .setMaxIter(10) + + // Convert indexed labels back to original labels. + val labelConverter = new IndexToString() + .setInputCol("prediction") + .setOutputCol("predictedLabel") + .setLabels(labelIndexer.labels) + + // Chain indexers and GBT in a Pipeline + val pipeline = new Pipeline() + .setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter)) + + // Train model. This also runs the indexers. + val model = pipeline.fit(trainingData) + + // Make predictions. + val predictions = model.transform(testData) + + // Select example rows to display. + predictions.select("predictedLabel", "label", "features").show(5) + + // Select (prediction, true label) and compute test error + val evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("indexedLabel") + .setPredictionCol("prediction") + .setMetricName("precision") + val accuracy = evaluator.evaluate(predictions) + println("Test Error = " + (1.0 - accuracy)) + + val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel] + println("Learned classification GBT model:\n" + gbtModel.toDebugString) + // $example off$ + + sc.stop() + } +} +// scalastyle:on println diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala new file mode 100644 index 0000000000..da1cd9c2ce --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala @@ -0,0 +1,85 @@ +/* + * 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.ml + +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkConf, SparkContext} +// $example on$ +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.evaluation.RegressionEvaluator +import org.apache.spark.ml.feature.VectorIndexer +import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor} +// $example off$ + +object GradientBoostedTreeRegressorExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("GradientBoostedTreeRegressorExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + val featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data) + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a GBT model. + val gbt = new GBTRegressor() + .setLabelCol("label") + .setFeaturesCol("indexedFeatures") + .setMaxIter(10) + + // Chain indexer and GBT in a Pipeline + val pipeline = new Pipeline() + .setStages(Array(featureIndexer, gbt)) + + // Train model. This also runs the indexer. + val model = pipeline.fit(trainingData) + + // Make predictions. + val predictions = model.transform(testData) + + // Select example rows to display. + predictions.select("prediction", "label", "features").show(5) + + // Select (prediction, true label) and compute test error + val evaluator = new RegressionEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("rmse") + val rmse = evaluator.evaluate(predictions) + println("Root Mean Squared Error (RMSE) on test data = " + rmse) + + val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel] + println("Learned regression GBT model:\n" + gbtModel.toDebugString) + // $example off$ + + sc.stop() + } +} +// scalastyle:on println diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala index 8e4f1b09a2..b46faea571 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala @@ -23,12 +23,14 @@ import java.util.concurrent.TimeUnit.{NANOSECONDS => NANO} import scopt.OptionParser import org.apache.spark.{SparkContext, SparkConf} +// $example on$ import org.apache.spark.examples.mllib.AbstractParams import org.apache.spark.ml.classification.{OneVsRest, LogisticRegression} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.evaluation.MulticlassMetrics import org.apache.spark.mllib.linalg.Vector import org.apache.spark.sql.DataFrame +// $example off$ import org.apache.spark.sql.SQLContext /** @@ -112,6 +114,7 @@ object OneVsRestExample { val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) + // $example on$ val inputData = sqlContext.read.format("libsvm").load(params.input) // compute the train/test split: if testInput is not provided use part of input. val data = params.testInput match { @@ -172,6 +175,7 @@ object OneVsRestExample { println("label\tfpr") println(fprs.map {case (label, fpr) => label + "\t" + fpr}.mkString("\n")) + // $example off$ sc.stop() } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala new file mode 100644 index 0000000000..e79176ca6c --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala @@ -0,0 +1,97 @@ +/* + * 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.ml + +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkConf, SparkContext} +// $example on$ +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier} +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator +import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer} +// $example off$ + +object RandomForestClassifierExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("RandomForestClassifierExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + // Index labels, adding metadata to the label column. + // Fit on whole dataset to include all labels in index. + val labelIndexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("indexedLabel") + .fit(data) + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + val featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data) + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a RandomForest model. + val rf = new RandomForestClassifier() + .setLabelCol("indexedLabel") + .setFeaturesCol("indexedFeatures") + .setNumTrees(10) + + // Convert indexed labels back to original labels. + val labelConverter = new IndexToString() + .setInputCol("prediction") + .setOutputCol("predictedLabel") + .setLabels(labelIndexer.labels) + + // Chain indexers and forest in a Pipeline + val pipeline = new Pipeline() + .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter)) + + // Train model. This also runs the indexers. + val model = pipeline.fit(trainingData) + + // Make predictions. + val predictions = model.transform(testData) + + // Select example rows to display. + predictions.select("predictedLabel", "label", "features").show(5) + + // Select (prediction, true label) and compute test error + val evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("indexedLabel") + .setPredictionCol("prediction") + .setMetricName("precision") + val accuracy = evaluator.evaluate(predictions) + println("Test Error = " + (1.0 - accuracy)) + + val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] + println("Learned classification forest model:\n" + rfModel.toDebugString) + // $example off$ + + sc.stop() + } +} +// scalastyle:on println diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala new file mode 100644 index 0000000000..acec1437a1 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala @@ -0,0 +1,84 @@ +/* + * 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.ml + +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkConf, SparkContext} +// $example on$ +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.evaluation.RegressionEvaluator +import org.apache.spark.ml.feature.VectorIndexer +import org.apache.spark.ml.regression.{RandomForestRegressionModel, RandomForestRegressor} +// $example off$ + +object RandomForestRegressorExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("RandomForestRegressorExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + val featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data) + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a RandomForest model. + val rf = new RandomForestRegressor() + .setLabelCol("label") + .setFeaturesCol("indexedFeatures") + + // Chain indexer and forest in a Pipeline + val pipeline = new Pipeline() + .setStages(Array(featureIndexer, rf)) + + // Train model. This also runs the indexer. + val model = pipeline.fit(trainingData) + + // Make predictions. + val predictions = model.transform(testData) + + // Select example rows to display. + predictions.select("prediction", "label", "features").show(5) + + // Select (prediction, true label) and compute test error + val evaluator = new RegressionEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("rmse") + val rmse = evaluator.evaluate(predictions) + println("Root Mean Squared Error (RMSE) on test data = " + rmse) + + val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel] + println("Learned regression forest model:\n" + rfModel.toDebugString) + // $example off$ + + sc.stop() + } +} +// scalastyle:on println |