SPARK-15899 <https://issues.apache.org/jira/browse/SPARK-15899> ?
On Wed, Aug 3, 2016 at 11:05 AM, Flavio <marchifla...@gmail.com> wrote:
> Hello everyone,
>
> I am try to run a very easy example but unfortunately I am stuck on the
> follow exception:
>
> Exception in thread "main" java.lang.IllegalArgumentException:
> java.net.URISyntaxException: Relative path in absolute URI: file: "absolute
> directory"
>
> I was wondering if anyone got this exception trying to run the examples on
> the spark git repo; actually the code I am try to run is the follow:
>
>
> //$example on$
> 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.Dataset;
> import org.apache.spark.sql.Row;
> import org.apache.spark.sql.SparkSession;
> //$example off$
>
> public class JavaRandomForestRegressorExample {
> public static void main(String[] args) {
> System.setProperty("hadoop.home.dir", "C:\\winutils");
>
> SparkSession spark = SparkSession
> .builder()
> .master("local[*]")
>
> .appName("JavaRandomForestRegressorExample")
> .getOrCreate();
>
> // $example on$
> // Load and parse the data file, converting it to a
> DataFrame.
> Dataset<Row> data =
> spark.read().format("libsvm").load("C:\\data\\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)
> Dataset<Row>[] splits = data.randomSplit(new double[] {
> 0.7, 0.3 });
> Dataset<Row> trainingData = splits[0];
> Dataset<Row> 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.
> Dataset<Row> 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());
> // $example off$
>
> spark.stop();
> }
> }
>
>
> Thanks to everyone for reading/answering!
>
> Flavio
>
>
>
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