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

Example 31 with StructField

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

the class MLContextFrameTest method testInputFrameAndMatrixOutputMatrix.

@Test
public void testInputFrameAndMatrixOutputMatrix() {
    System.out.println("MLContextFrameTest - input frame and matrix, output matrix");
    List<String> dataA = new ArrayList<String>();
    dataA.add("Test1,4.0");
    dataA.add("Test2,5.0");
    dataA.add("Test3,6.0");
    JavaRDD<String> javaRddStringA = sc.parallelize(dataA);
    ValueType[] schema = { ValueType.STRING, ValueType.DOUBLE };
    List<String> dataB = new ArrayList<String>();
    dataB.add("1.0");
    dataB.add("2.0");
    JavaRDD<String> javaRddStringB = sc.parallelize(dataB);
    JavaRDD<Row> javaRddRowA = FrameRDDConverterUtils.csvToRowRDD(sc, javaRddStringA, CSV_DELIM, schema);
    JavaRDD<Row> javaRddRowB = javaRddStringB.map(new CommaSeparatedValueStringToDoubleArrayRow());
    List<StructField> fieldsA = new ArrayList<StructField>();
    fieldsA.add(DataTypes.createStructField("1", DataTypes.StringType, true));
    fieldsA.add(DataTypes.createStructField("2", DataTypes.DoubleType, true));
    StructType schemaA = DataTypes.createStructType(fieldsA);
    Dataset<Row> dataFrameA = spark.createDataFrame(javaRddRowA, schemaA);
    List<StructField> fieldsB = new ArrayList<StructField>();
    fieldsB.add(DataTypes.createStructField("1", DataTypes.DoubleType, true));
    StructType schemaB = DataTypes.createStructType(fieldsB);
    Dataset<Row> dataFrameB = spark.createDataFrame(javaRddRowB, schemaB);
    String dmlString = "[tA, tAM] = transformencode (target = A, spec = \"{ids: true ,recode: [ 1, 2 ]}\");\n" + "C = tA %*% B;\n" + "M = s * C;";
    Script script = dml(dmlString).in("A", dataFrameA, new FrameMetadata(FrameFormat.CSV, dataFrameA.count(), (long) dataFrameA.columns().length)).in("B", dataFrameB, new MatrixMetadata(MatrixFormat.CSV, dataFrameB.count(), (long) dataFrameB.columns().length)).in("s", 2).out("M");
    MLResults results = ml.execute(script);
    double[][] matrix = results.getMatrixAs2DDoubleArray("M");
    Assert.assertEquals(6.0, matrix[0][0], 0.0);
    Assert.assertEquals(12.0, matrix[1][0], 0.0);
    Assert.assertEquals(18.0, matrix[2][0], 0.0);
}

Example 32 with StructField

use of org.apache.spark.sql.types.StructField in project net.jgp.labs.spark by jgperrin.

the class StreamingIngestionFileSystemTextFileToDataframeApp method start.

private void start() {
    // Create a local StreamingContext with two working thread and batch interval of
    // 1 second
    SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("Streaming Ingestion File System Text File to Dataframe");
    JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));
    JavaDStream<String> msgDataStream = jssc.textFileStream(StreamingUtils.getInputDirectory());
    msgDataStream.print();
    // Create JavaRDD<Row>
    msgDataStream.foreachRDD(new VoidFunction<JavaRDD<String>>() {

        private static final long serialVersionUID = -590010339928376829L;

        @Override
        public void call(JavaRDD<String> rdd) {
            JavaRDD<Row> rowRDD = rdd.map(new Function<String, Row>() {

                private static final long serialVersionUID = 5167089361335095997L;

                @Override
                public Row call(String msg) {
                    Row row = RowFactory.create(msg);
                    return row;
                }
            });
            // Create Schema
            StructType schema = DataTypes.createStructType(new StructField[] { DataTypes.createStructField("Message", DataTypes.StringType, true) });
            // Get Spark 2.0 session
            SparkSession spark = JavaSparkSessionSingleton.getInstance(rdd.context().getConf());
            Dataset<Row> msgDataFrame = spark.createDataFrame(rowRDD, schema);
            msgDataFrame.show();
        }
    });
    jssc.start();
    try {
        jssc.awaitTermination();
    } catch (InterruptedException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
}

Example 33 with StructField

use of org.apache.spark.sql.types.StructField in project net.jgp.labs.spark by jgperrin.

the class SimplePredictionFromTextFile method start.

private void start() {
    SparkSession spark = SparkSession.builder().appName("Simple prediction from Text File").master("local").getOrCreate();
    spark.udf().register("vectorBuilder", new VectorBuilder(), new VectorUDT());
    String filename = "data/tuple-data-file.csv";
    StructType schema = new StructType(new StructField[] { new StructField("_c0", DataTypes.DoubleType, false, Metadata.empty()), new StructField("_c1", DataTypes.DoubleType, false, Metadata.empty()), new StructField("features", new VectorUDT(), true, Metadata.empty()) });
    Dataset<Row> df = spark.read().format("csv").schema(schema).option("header", "false").load(filename);
    df = df.withColumn("valuefeatures", df.col("_c0")).drop("_c0");
    df = df.withColumn("label", df.col("_c1")).drop("_c1");
    df.printSchema();
    df = df.withColumn("features", callUDF("vectorBuilder", df.col("valuefeatures")));
    df.printSchema();
    df.show();
    // .setRegParam(1).setElasticNetParam(1);
    LinearRegression lr = new LinearRegression().setMaxIter(20);
    // Fit the model to the data.
    LinearRegressionModel model = lr.fit(df);
    // Given a dataset, predict each point's label, and show the results.
    model.transform(df).show();
    LinearRegressionTrainingSummary trainingSummary = model.summary();
    System.out.println("numIterations: " + trainingSummary.totalIterations());
    System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
    trainingSummary.residuals().show();
    System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
    System.out.println("r2: " + trainingSummary.r2());
    double intercept = model.intercept();
    System.out.println("Interesection: " + intercept);
    double regParam = model.getRegParam();
    System.out.println("Regression parameter: " + regParam);
    double tol = model.getTol();
    System.out.println("Tol: " + tol);
    Double feature = 7.0;
    Vector features = Vectors.dense(feature);
    double p = model.predict(features);
    System.out.println("Prediction for feature " + feature + " is " + p);
    System.out.println(8 * regParam + intercept);
}

Example 34 with StructField

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

the class MLContextUtil method doesDataFrameLookLikeMatrix.

/**
 * Examine the DataFrame schema to determine whether the data appears to be
 * a matrix.
 *
 * @param df
 *            the DataFrame
 * @return {@code true} if the DataFrame appears to be a matrix,
 *         {@code false} otherwise
 */
public static boolean doesDataFrameLookLikeMatrix(Dataset<Row> df) {
    StructType schema = df.schema();
    StructField[] fields = schema.fields();
    if (fields == null) {
        return true;
    }
    for (StructField field : fields) {
        DataType dataType = field.dataType();
        if ((dataType != DataTypes.DoubleType) && (dataType != DataTypes.IntegerType) && (dataType != DataTypes.LongType) && (!(dataType instanceof org.apache.spark.ml.linalg.VectorUDT)) && (!(dataType instanceof org.apache.spark.mllib.linalg.VectorUDT))) {
            // }
            return false;
        }
    }
    return true;
}

Example 35 with StructField

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

the class MLContextTest method testDataFrameSumPYDMLDoublesWithNoIDColumn.

@Test
public void testDataFrameSumPYDMLDoublesWithNoIDColumn() {
    System.out.println("MLContextTest - DataFrame sum PYDML, 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 = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
    setExpectedStdOut("sum: 450.0");
    ml.execute(script);
}