org.apache.spark.ml.feature.VectorSlicer Java Examples
The following examples show how to use
org.apache.spark.ml.feature.VectorSlicer.
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Example #1
Source File: JavaVectorSlicerExample.java From SparkDemo with MIT License | 5 votes |
public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaVectorSlicerExample") .getOrCreate(); // $example on$ Attribute[] attrs = new Attribute[]{ NumericAttribute.defaultAttr().withName("f1"), NumericAttribute.defaultAttr().withName("f2"), NumericAttribute.defaultAttr().withName("f3") }; AttributeGroup group = new AttributeGroup("userFeatures", attrs); List<Row> data = Lists.newArrayList( RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})), RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0)) ); Dataset<Row> dataset = spark.createDataFrame(data, (new StructType()).add(group.toStructField())); VectorSlicer vectorSlicer = new VectorSlicer() .setInputCol("userFeatures").setOutputCol("features"); vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"}); // or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"}) Dataset<Row> output = vectorSlicer.transform(dataset); output.show(false); // $example off$ spark.stop(); }
Example #2
Source File: VectorSlicerConverter.java From jpmml-sparkml with GNU Affero General Public License v3.0 | 5 votes |
@Override public List<Feature> encodeFeatures(SparkMLEncoder encoder){ VectorSlicer transformer = getTransformer(); String[] names = transformer.getNames(); if(names != null && names.length > 0){ throw new IllegalArgumentException("Expected index mode, got name mode"); } return encoder.getFeatures(transformer.getInputCol(), transformer.getIndices()); }
Example #3
Source File: VectorSlicerConverter.java From jpmml-sparkml with GNU Affero General Public License v3.0 | 4 votes |
public VectorSlicerConverter(VectorSlicer transformer){ super(transformer); }