Ho Sean, thanks for the reply. I just omitted the real path which would
point on my file system. I just posted the real one.
On 3 Aug 2016 19:09, "Sean Owen" <so...@cloudera.com> wrote:
> file: "absolute directory"
> does not sound like a valid URI
>
> 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
> >
> >
> >
> > --
> > View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/java-net-URISyntaxException-Relative-path-in-absolute-URI-tp27466.html
> > Sent from the Apache Spark User List mailing list archive at Nabble.com.
> >
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> >
>
