Examples with StructType org.apache.spark.sql.types.StructType used on opensource projects

Example 26 with StructType

use of org.apache.spark.sql.types.StructType in project incubator-systemml by apache.

the class MLContextTest method testInputBinaryBlockMatrixDML.

@Test
public void testInputBinaryBlockMatrixDML() {
    System.out.println("MLContextTest - input BinaryBlockMatrix DML");
    List<String> list = new ArrayList<String>();
    list.add("10,20,30");
    list.add("40,50,60");
    list.add("70,80,90");
    JavaRDD<String> javaRddString = sc.parallelize(list);
    JavaRDD<Row> javaRddRow = javaRddString.map(new CommaSeparatedValueStringToRow());
    List<StructField> fields = new ArrayList<StructField>();
    fields.add(DataTypes.createStructField("C1", DataTypes.StringType, true));
    fields.add(DataTypes.createStructField("C2", DataTypes.StringType, true));
    fields.add(DataTypes.createStructField("C3", DataTypes.StringType, true));
    StructType schema = DataTypes.createStructType(fields);
    Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
    BinaryBlockMatrix binaryBlockMatrix = new BinaryBlockMatrix(dataFrame);
    Script script = dml("avg = avg(M);").in("M", binaryBlockMatrix).out("avg");
    double avg = ml.execute(script).getDouble("avg");
    Assert.assertEquals(50.0, avg, 0.0);
}

Example 27 with StructType

use of org.apache.spark.sql.types.StructType in project incubator-systemml by apache.

the class MLContextTest method testDataFrameSumDMLDoublesWithNoIDColumn.

@Test
public void testDataFrameSumDMLDoublesWithNoIDColumn() {
    System.out.println("MLContextTest - DataFrame sum DML, doubles with no ID column");
    List<String> list = new ArrayList<String>();
    list.add("10,20,30");
    list.add("40,50,60");
    list.add("70,80,90");
    JavaRDD<String> javaRddString = sc.parallelize(list);
    JavaRDD<Row> javaRddRow = javaRddString.map(new CommaSeparatedValueStringToDoubleArrayRow());
    List<StructField> fields = new ArrayList<StructField>();
    fields.add(DataTypes.createStructField("C1", DataTypes.DoubleType, true));
    fields.add(DataTypes.createStructField("C2", DataTypes.DoubleType, true));
    fields.add(DataTypes.createStructField("C3", DataTypes.DoubleType, true));
    StructType schema = DataTypes.createStructType(fields);
    Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
    MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_DOUBLES);
    Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame, mm);
    setExpectedStdOut("sum: 450.0");
    ml.execute(script);
}

Example 28 with StructType

use of org.apache.spark.sql.types.StructType 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 29 with StructType

use of org.apache.spark.sql.types.StructType in project incubator-systemml by apache.

the class FrameRDDConverterUtils method binaryBlockToDataFrame.

public static Dataset<Row> binaryBlockToDataFrame(SparkSession sparkSession, JavaPairRDD<Long, FrameBlock> in, MatrixCharacteristics mc, ValueType[] schema) {
    if (!mc.colsKnown())
        throw new RuntimeException("Number of columns needed to convert binary block to data frame.");
    //convert binary block to rows rdd 
    JavaRDD<Row> rowRDD = in.flatMap(new BinaryBlockToDataFrameFunction());
    //create data frame schema
    if (schema == null)
        schema = UtilFunctions.nCopies((int) mc.getCols(), ValueType.STRING);
    StructType dfSchema = convertFrameSchemaToDFSchema(schema, true);
    //rdd to data frame conversion
    return sparkSession.createDataFrame(rowRDD, dfSchema);
}

Example 30 with StructType

use of org.apache.spark.sql.types.StructType in project incubator-systemml by apache.

the class MLContextConversionUtil method determineFrameFormatIfNeeded.

/**
	 * If the FrameFormat of the DataFrame has not been explicitly specified,
	 * attempt to determine the proper FrameFormat.
	 *
	 * @param dataFrame
	 *            the Spark {@code DataFrame}
	 * @param frameMetadata
	 *            the frame metadata, if available
	 */
public static void determineFrameFormatIfNeeded(Dataset<Row> dataFrame, FrameMetadata frameMetadata) {
    FrameFormat frameFormat = frameMetadata.getFrameFormat();
    if (frameFormat != null) {
        return;
    }
    StructType schema = dataFrame.schema();
    boolean hasID = false;
    try {
        schema.fieldIndex(RDDConverterUtils.DF_ID_COLUMN);
        hasID = true;
    } catch (IllegalArgumentException iae) {
    }
    FrameFormat ff = hasID ? FrameFormat.DF_WITH_INDEX : FrameFormat.DF;
    frameMetadata.setFrameFormat(ff);
}