Examples with Vector org.apache.spark.ml.linalg.Vector used on opensource projects

Example 6 with Vector

use of org.apache.spark.ml.linalg.Vector in project incubator-systemml by apache.

the class MLContextTest method testDataFrameSumDMLMllibVectorWithNoIDColumn.

@Test
public void testDataFrameSumDMLMllibVectorWithNoIDColumn() {
    System.out.println("MLContextTest - DataFrame sum DML, mllib vector with no ID column");
    List<org.apache.spark.mllib.linalg.Vector> list = new ArrayList<org.apache.spark.mllib.linalg.Vector>();
    list.add(org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0));
    list.add(org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0));
    list.add(org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0));
    JavaRDD<org.apache.spark.mllib.linalg.Vector> javaRddVector = sc.parallelize(list);
    JavaRDD<Row> javaRddRow = javaRddVector.map(new MllibVectorRow());
    List<StructField> fields = new ArrayList<StructField>();
    fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
    StructType schema = DataTypes.createStructType(fields);
    Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
    MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);
    Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame, mm);
    setExpectedStdOut("sum: 45.0");
    ml.execute(script);
}

Example 7 with Vector

use of org.apache.spark.ml.linalg.Vector in project incubator-systemml by apache.

the class MLContextTest method testDataFrameSumPYDMLVectorWithNoIDColumn.

@Test
public void testDataFrameSumPYDMLVectorWithNoIDColumn() {
    System.out.println("MLContextTest - DataFrame sum PYDML, vector with no ID column");
    List<Vector> list = new ArrayList<Vector>();
    list.add(Vectors.dense(1.0, 2.0, 3.0));
    list.add(Vectors.dense(4.0, 5.0, 6.0));
    list.add(Vectors.dense(7.0, 8.0, 9.0));
    JavaRDD<Vector> javaRddVector = sc.parallelize(list);
    JavaRDD<Row> javaRddRow = javaRddVector.map(new VectorRow());
    List<StructField> fields = new ArrayList<StructField>();
    fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
    StructType schema = DataTypes.createStructType(fields);
    Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
    MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);
    Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
    setExpectedStdOut("sum: 45.0");
    ml.execute(script);
}

Example 8 with Vector

use of org.apache.spark.ml.linalg.Vector in project incubator-systemml by apache.

the class RDDConverterUtilsExt method stringDataFrameToVectorDataFrame.

/**
	 * Convert a dataframe of comma-separated string rows to a dataframe of
	 * ml.linalg.Vector rows.
	 *
	 * <p>
	 * Example input rows:<br>
	 *
	 * <code>
	 * ((1.2, 4.3, 3.4))<br>
	 * (1.2, 3.4, 2.2)<br>
	 * [[1.2, 34.3, 1.2, 1.25]]<br>
	 * [1.2, 3.4]<br>
	 * </code>
	 *
	 * @param sparkSession
	 *            Spark Session
	 * @param inputDF
	 *            dataframe of comma-separated row strings to convert to
	 *            dataframe of ml.linalg.Vector rows
	 * @return dataframe of ml.linalg.Vector rows
	 * @throws DMLRuntimeException
	 *             if DMLRuntimeException occurs
	 */
public static Dataset<Row> stringDataFrameToVectorDataFrame(SparkSession sparkSession, Dataset<Row> inputDF) throws DMLRuntimeException {
    StructField[] oldSchema = inputDF.schema().fields();
    StructField[] newSchema = new StructField[oldSchema.length];
    for (int i = 0; i < oldSchema.length; i++) {
        String colName = oldSchema[i].name();
        newSchema[i] = DataTypes.createStructField(colName, new VectorUDT(), true);
    }
    // converter
    class StringToVector implements Function<Tuple2<Row, Long>, Row> {

        private static final long serialVersionUID = -4733816995375745659L;

        @Override
        public Row call(Tuple2<Row, Long> arg0) throws Exception {
            Row oldRow = arg0._1;
            int oldNumCols = oldRow.length();
            if (oldNumCols > 1) {
                throw new DMLRuntimeException("The row must have at most one column");
            }
            // parse the various strings. i.e
            // ((1.2, 4.3, 3.4)) or (1.2, 3.4, 2.2)
            // [[1.2, 34.3, 1.2, 1.2]] or [1.2, 3.4]
            Object[] fields = new Object[oldNumCols];
            ArrayList<Object> fieldsArr = new ArrayList<Object>();
            for (int i = 0; i < oldRow.length(); i++) {
                Object ci = oldRow.get(i);
                if (ci == null) {
                    fieldsArr.add(null);
                } else if (ci instanceof String) {
                    String cis = (String) ci;
                    StringBuffer sb = new StringBuffer(cis.trim());
                    for (int nid = 0; i < 2; i++) {
                        // nesting
                        if ((sb.charAt(0) == '(' && sb.charAt(sb.length() - 1) == ')') || (sb.charAt(0) == '[' && sb.charAt(sb.length() - 1) == ']')) {
                            sb.deleteCharAt(0);
                            sb.setLength(sb.length() - 1);
                        }
                    }
                    // have the replace code
                    String ncis = "[" + sb.toString().replaceAll(" *, *", ",") + "]";
                    try {
                        // ncis [ ] will always result in double array return type
                        double[] doubles = (double[]) NumericParser.parse(ncis);
                        Vector dense = Vectors.dense(doubles);
                        fieldsArr.add(dense);
                    } catch (Exception e) {
                        // can't catch SparkException here in Java apparently
                        throw new DMLRuntimeException("Error converting to double array. " + e.getMessage(), e);
                    }
                } else {
                    throw new DMLRuntimeException("Only String is supported");
                }
            }
            Row row = RowFactory.create(fieldsArr.toArray());
            return row;
        }
    }
    // output DF
    JavaRDD<Row> newRows = inputDF.rdd().toJavaRDD().zipWithIndex().map(new StringToVector());
    Dataset<Row> outDF = sparkSession.createDataFrame(newRows.rdd(), DataTypes.createStructType(newSchema));
    return outDF;
}

Example 9 with Vector

use of org.apache.spark.ml.linalg.Vector in project incubator-systemml by apache.

the class MLContextTest method testDataFrameSumPYDMLMllibVectorWithIDColumn.

@Test
public void testDataFrameSumPYDMLMllibVectorWithIDColumn() {
    System.out.println("MLContextTest - DataFrame sum PYDML, mllib vector with ID column");
    List<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>> list = new ArrayList<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>>();
    list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(1.0, org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0)));
    list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(2.0, org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0)));
    list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(3.0, org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0)));
    JavaRDD<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>> javaRddTuple = sc.parallelize(list);
    JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleMllibVectorRow());
    List<StructField> fields = new ArrayList<StructField>();
    fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
    fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
    StructType schema = DataTypes.createStructType(fields);
    Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
    MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR_WITH_INDEX);
    Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
    setExpectedStdOut("sum: 45.0");
    ml.execute(script);
}

Example 10 with Vector

use of org.apache.spark.ml.linalg.Vector in project incubator-systemml by apache.

the class MLContextTest method testDataFrameSumPYDMLMllibVectorWithNoIDColumn.

@Test
public void testDataFrameSumPYDMLMllibVectorWithNoIDColumn() {
    System.out.println("MLContextTest - DataFrame sum PYDML, mllib vector with no ID column");
    List<org.apache.spark.mllib.linalg.Vector> list = new ArrayList<org.apache.spark.mllib.linalg.Vector>();
    list.add(org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0));
    list.add(org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0));
    list.add(org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0));
    JavaRDD<org.apache.spark.mllib.linalg.Vector> javaRddVector = sc.parallelize(list);
    JavaRDD<Row> javaRddRow = javaRddVector.map(new MllibVectorRow());
    List<StructField> fields = new ArrayList<StructField>();
    fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
    StructType schema = DataTypes.createStructType(fields);
    Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
    MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);
    Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
    setExpectedStdOut("sum: 45.0");
    ml.execute(script);
}