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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