From 9e266d07a444fd465fe178cdd5c4894cd09cbda3 Mon Sep 17 00:00:00 2001 From: Zheng RuiFeng Date: Wed, 11 May 2016 22:45:30 -0700 Subject: [SPARK-15031][SPARK-15134][EXAMPLE][DOC] Use SparkSession and update indent in examples ## What changes were proposed in this pull request? 1, Use `SparkSession` according to [SPARK-15031](https://issues.apache.org/jira/browse/SPARK-15031) 2, Update indent for `SparkContext` according to [SPARK-15134](https://issues.apache.org/jira/browse/SPARK-15134) 3, BTW, remove some duplicate space and add missing '.' ## How was this patch tested? manual tests Author: Zheng RuiFeng Closes #13050 from zhengruifeng/use_sparksession. --- .../examples/ml/JavaDecisionTreeClassificationExample.java | 14 ++++++++------ .../examples/ml/JavaDecisionTreeRegressionExample.java | 12 +++++++----- .../apache/spark/examples/ml/JavaDeveloperApiExample.java | 6 +++--- .../examples/ml/JavaEstimatorTransformerParamExample.java | 4 +++- .../ml/JavaGradientBoostedTreeClassifierExample.java | 6 +++--- .../ml/JavaGradientBoostedTreeRegressorExample.java | 12 +++++++----- .../ml/JavaLinearRegressionWithElasticNetExample.java | 12 +++++++----- .../examples/ml/JavaLogisticRegressionSummaryExample.java | 4 +++- .../ml/JavaLogisticRegressionWithElasticNetExample.java | 4 +++- .../ml/JavaModelSelectionViaCrossValidationExample.java | 4 +++- .../JavaModelSelectionViaTrainValidationSplitExample.java | 4 +++- .../ml/JavaMultilayerPerceptronClassifierExample.java | 4 +++- .../spark/examples/ml/JavaQuantileDiscretizerExample.java | 4 +++- .../examples/ml/JavaRandomForestClassifierExample.java | 4 +++- .../examples/ml/JavaRandomForestRegressorExample.java | 6 ++++-- .../apache/spark/examples/ml/JavaSimpleParamsExample.java | 8 ++++---- .../examples/ml/JavaSimpleTextClassificationPipeline.java | 4 +++- 17 files changed, 70 insertions(+), 42 deletions(-) (limited to 'examples/src/main/java') diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java index 733bc4181c..bdb76f004f 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java @@ -32,7 +32,9 @@ import org.apache.spark.sql.SparkSession; public class JavaDecisionTreeClassificationExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaDecisionTreeClassificationExample").getOrCreate(); + .builder() + .appName("JavaDecisionTreeClassificationExample") + .getOrCreate(); // $example on$ // Load the data stored in LIBSVM format as a DataFrame. @@ -52,10 +54,10 @@ public class JavaDecisionTreeClassificationExample { VectorIndexerModel featureIndexer = new VectorIndexer() .setInputCol("features") .setOutputCol("indexedFeatures") - .setMaxCategories(4) // features with > 4 distinct values are treated as continuous + .setMaxCategories(4) // features with > 4 distinct values are treated as continuous. .fit(data); - // Split the data into training and test sets (30% held out for testing) + // Split the data into training and test sets (30% held out for testing). Dataset[] splits = data.randomSplit(new double[]{0.7, 0.3}); Dataset trainingData = splits[0]; Dataset testData = splits[1]; @@ -71,11 +73,11 @@ public class JavaDecisionTreeClassificationExample { .setOutputCol("predictedLabel") .setLabels(labelIndexer.labels()); - // Chain indexers and tree in a Pipeline + // Chain indexers and tree in a Pipeline. Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter}); - // Train model. This also runs the indexers. + // Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData); // Make predictions. @@ -84,7 +86,7 @@ public class JavaDecisionTreeClassificationExample { // Select example rows to display. predictions.select("predictedLabel", "label", "features").show(5); - // Select (prediction, true label) and compute test error + // Select (prediction, true label) and compute test error. MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setLabelCol("indexedLabel") .setPredictionCol("prediction") diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java index bd6dc3edd3..cffb7139ed 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java @@ -33,7 +33,9 @@ import org.apache.spark.sql.SparkSession; public class JavaDecisionTreeRegressionExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaDecisionTreeRegressionExample").getOrCreate(); + .builder() + .appName("JavaDecisionTreeRegressionExample") + .getOrCreate(); // $example on$ // Load the data stored in LIBSVM format as a DataFrame. Dataset data = spark.read().format("libsvm") @@ -47,7 +49,7 @@ public class JavaDecisionTreeRegressionExample { .setMaxCategories(4) .fit(data); - // Split the data into training and test sets (30% held out for testing) + // Split the data into training and test sets (30% held out for testing). Dataset[] splits = data.randomSplit(new double[]{0.7, 0.3}); Dataset trainingData = splits[0]; Dataset testData = splits[1]; @@ -56,11 +58,11 @@ public class JavaDecisionTreeRegressionExample { DecisionTreeRegressor dt = new DecisionTreeRegressor() .setFeaturesCol("indexedFeatures"); - // Chain indexer and tree in a Pipeline + // Chain indexer and tree in a Pipeline. Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{featureIndexer, dt}); - // Train model. This also runs the indexer. + // Train model. This also runs the indexer. PipelineModel model = pipeline.fit(trainingData); // Make predictions. @@ -69,7 +71,7 @@ public class JavaDecisionTreeRegressionExample { // Select example rows to display. predictions.select("label", "features").show(5); - // Select (prediction, true label) and compute test error + // Select (prediction, true label) and compute test error. RegressionEvaluator evaluator = new RegressionEvaluator() .setLabelCol("label") .setPredictionCol("prediction") diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index 49bad0afc0..3265c4d7ec 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -62,7 +62,7 @@ public class JavaDeveloperApiExample { new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); Dataset training = spark.createDataFrame(localTraining, LabeledPoint.class); - // Create a LogisticRegression instance. This instance is an Estimator. + // Create a LogisticRegression instance. This instance is an Estimator. MyJavaLogisticRegression lr = new MyJavaLogisticRegression(); // Print out the parameters, documentation, and any default values. System.out.println("MyJavaLogisticRegression parameters:\n" + lr.explainParams() + "\n"); @@ -70,7 +70,7 @@ public class JavaDeveloperApiExample { // We may set parameters using setter methods. lr.setMaxIter(10); - // Learn a LogisticRegression model. This uses the parameters stored in lr. + // Learn a LogisticRegression model. This uses the parameters stored in lr. MyJavaLogisticRegressionModel model = lr.fit(training); // Prepare test data. @@ -214,7 +214,7 @@ class MyJavaLogisticRegressionModel } /** - * Number of classes the label can take. 2 indicates binary classification. + * Number of classes the label can take. 2 indicates binary classification. */ public int numClasses() { return 2; } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java index 5ba8e6cf44..889f5785df 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java @@ -38,7 +38,9 @@ import org.apache.spark.sql.SparkSession; public class JavaEstimatorTransformerParamExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaEstimatorTransformerParamExample").getOrCreate(); + .builder() + .appName("JavaEstimatorTransformerParamExample") + .getOrCreate(); // $example on$ // Prepare training data. 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 index baacd796a0..5c2e03eda9 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java @@ -75,11 +75,11 @@ public class JavaGradientBoostedTreeClassifierExample { .setOutputCol("predictedLabel") .setLabels(labelIndexer.labels()); - // Chain indexers and GBT in a Pipeline + // 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. + // Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData); // Make predictions. @@ -88,7 +88,7 @@ public class JavaGradientBoostedTreeClassifierExample { // Select example rows to display. predictions.select("predictedLabel", "label", "features").show(5); - // Select (prediction, true label) and compute test error + // Select (prediction, true label) and compute test error. MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setLabelCol("indexedLabel") .setPredictionCol("prediction") 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 index 6d3f21fdaf..769b5c3e85 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java @@ -34,7 +34,9 @@ import org.apache.spark.sql.SparkSession; public class JavaGradientBoostedTreeRegressorExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaGradientBoostedTreeRegressorExample").getOrCreate(); + .builder() + .appName("JavaGradientBoostedTreeRegressorExample") + .getOrCreate(); // $example on$ // Load and parse the data file, converting it to a DataFrame. @@ -48,7 +50,7 @@ public class JavaGradientBoostedTreeRegressorExample { .setMaxCategories(4) .fit(data); - // Split the data into training and test sets (30% held out for testing) + // Split the data into training and test sets (30% held out for testing). Dataset[] splits = data.randomSplit(new double[] {0.7, 0.3}); Dataset trainingData = splits[0]; Dataset testData = splits[1]; @@ -59,10 +61,10 @@ public class JavaGradientBoostedTreeRegressorExample { .setFeaturesCol("indexedFeatures") .setMaxIter(10); - // Chain indexer and GBT in a Pipeline + // Chain indexer and GBT in a Pipeline. Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {featureIndexer, gbt}); - // Train model. This also runs the indexer. + // Train model. This also runs the indexer. PipelineModel model = pipeline.fit(trainingData); // Make predictions. @@ -71,7 +73,7 @@ public class JavaGradientBoostedTreeRegressorExample { // Select example rows to display. predictions.select("prediction", "label", "features").show(5); - // Select (prediction, true label) and compute test error + // Select (prediction, true label) and compute test error. RegressionEvaluator evaluator = new RegressionEvaluator() .setLabelCol("label") .setPredictionCol("prediction") diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java index b6ea1fed25..dcd209e28e 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java @@ -30,10 +30,12 @@ import org.apache.spark.sql.SparkSession; public class JavaLinearRegressionWithElasticNetExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaLinearRegressionWithElasticNetExample").getOrCreate(); + .builder() + .appName("JavaLinearRegressionWithElasticNetExample") + .getOrCreate(); // $example on$ - // Load training data + // Load training data. Dataset training = spark.read().format("libsvm") .load("data/mllib/sample_linear_regression_data.txt"); @@ -42,14 +44,14 @@ public class JavaLinearRegressionWithElasticNetExample { .setRegParam(0.3) .setElasticNetParam(0.8); - // Fit the model + // Fit the model. LinearRegressionModel lrModel = lr.fit(training); - // Print the coefficients and intercept for linear regression + // Print the coefficients and intercept for linear regression. System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept()); - // Summarize the model over the training set and print out some metrics + // Summarize the model over the training set and print out some metrics. LinearRegressionTrainingSummary trainingSummary = lrModel.summary(); System.out.println("numIterations: " + trainingSummary.totalIterations()); System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory())); diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java index fd040aead4..dee56799d8 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java @@ -31,7 +31,9 @@ import org.apache.spark.sql.functions; public class JavaLogisticRegressionSummaryExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaLogisticRegressionSummaryExample").getOrCreate(); + .builder() + .appName("JavaLogisticRegressionSummaryExample") + .getOrCreate(); // Load training data Dataset training = spark.read().format("libsvm") diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java index f00c7a05cd..6101c79fb0 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java @@ -28,7 +28,9 @@ import org.apache.spark.sql.SparkSession; public class JavaLogisticRegressionWithElasticNetExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaLogisticRegressionWithElasticNetExample").getOrCreate(); + .builder() + .appName("JavaLogisticRegressionWithElasticNetExample") + .getOrCreate(); // $example on$ // Load training data diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java index a4ec4f5815..975c65edc0 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java @@ -43,7 +43,9 @@ import org.apache.spark.sql.SparkSession; public class JavaModelSelectionViaCrossValidationExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaModelSelectionViaCrossValidationExample").getOrCreate(); + .builder() + .appName("JavaModelSelectionViaCrossValidationExample") + .getOrCreate(); // $example on$ // Prepare training documents, which are labeled. diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java index 63a0ad1cb8..0f96293f03 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java @@ -43,7 +43,9 @@ import org.apache.spark.sql.SparkSession; public class JavaModelSelectionViaTrainValidationSplitExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaModelSelectionViaTrainValidationSplitExample").getOrCreate(); + .builder() + .appName("JavaModelSelectionViaTrainValidationSplitExample") + .getOrCreate(); // $example on$ Dataset data = spark.read().format("libsvm") diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java index d547a2a64b..c7d03d8593 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java @@ -33,7 +33,9 @@ public class JavaMultilayerPerceptronClassifierExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaMultilayerPerceptronClassifierExample").getOrCreate(); + .builder() + .appName("JavaMultilayerPerceptronClassifierExample") + .getOrCreate(); // $example on$ // Load training data diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java index 94e3fafcab..16f58a852d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java @@ -35,7 +35,9 @@ import org.apache.spark.sql.types.StructType; public class JavaQuantileDiscretizerExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaQuantileDiscretizerExample").getOrCreate(); + .builder() + .appName("JavaQuantileDiscretizerExample") + .getOrCreate(); // $example on$ List data = Arrays.asList( 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 index 21e783a968..14af2fbbbb 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java @@ -33,7 +33,9 @@ import org.apache.spark.sql.SparkSession; public class JavaRandomForestClassifierExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaRandomForestClassifierExample").getOrCreate(); + .builder() + .appName("JavaRandomForestClassifierExample") + .getOrCreate(); // $example on$ // Load and parse the data file, converting it to a DataFrame. 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 index ece184a878..a7078453de 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java @@ -34,7 +34,9 @@ import org.apache.spark.sql.SparkSession; public class JavaRandomForestRegressorExample { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaRandomForestRegressorExample").getOrCreate(); + .builder() + .appName("JavaRandomForestRegressorExample") + .getOrCreate(); // $example on$ // Load and parse the data file, converting it to a DataFrame. @@ -62,7 +64,7 @@ public class JavaRandomForestRegressorExample { Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[] {featureIndexer, rf}); - // Train model. This also runs the indexer. + // Train model. This also runs the indexer. PipelineModel model = pipeline.fit(trainingData); // Make predictions. diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index 0787079ba4..ff1eb07dc6 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -46,7 +46,7 @@ public class JavaSimpleParamsExample { .getOrCreate(); // Prepare training data. - // We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans + // We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans // into DataFrames, where it uses the bean metadata to infer the schema. List localTraining = Lists.newArrayList( new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), @@ -56,7 +56,7 @@ public class JavaSimpleParamsExample { Dataset training = spark.createDataFrame(localTraining, LabeledPoint.class); - // Create a LogisticRegression instance. This instance is an Estimator. + // Create a LogisticRegression instance. This instance is an Estimator. LogisticRegression lr = new LogisticRegression(); // Print out the parameters, documentation, and any default values. System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n"); @@ -65,7 +65,7 @@ public class JavaSimpleParamsExample { lr.setMaxIter(10) .setRegParam(0.01); - // Learn a LogisticRegression model. This uses the parameters stored in lr. + // Learn a LogisticRegression model. This uses the parameters stored in lr. LogisticRegressionModel model1 = lr.fit(training); // Since model1 is a Model (i.e., a Transformer produced by an Estimator), // we can view the parameters it used during fit(). @@ -82,7 +82,7 @@ public class JavaSimpleParamsExample { // One can also combine ParamMaps. ParamMap paramMap2 = new ParamMap(); - paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name + paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name. ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2); // Now learn a new model using the paramMapCombined parameters. diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java index 9516ce1f4f..7c24c46d2e 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java @@ -43,7 +43,9 @@ public class JavaSimpleTextClassificationPipeline { public static void main(String[] args) { SparkSession spark = SparkSession - .builder().appName("JavaSimpleTextClassificationPipeline").getOrCreate(); + .builder() + .appName("JavaSimpleTextClassificationPipeline") + .getOrCreate(); // Prepare training documents, which are labeled. List localTraining = Lists.newArrayList( -- cgit v1.2.3