java.net.URISyntaxException: Relative path in absolute URI:

[Flavio]

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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