Examples with FrameSchema org.apache.sysml.api.mlcontext.FrameSchema used on opensource projects

Example 1 with FrameSchema

use of org.apache.sysml.api.mlcontext.FrameSchema in project systemml by apache.

the class FrameTest method testFrameGeneral.

private void testFrameGeneral(InputInfo iinfo, OutputInfo oinfo, boolean bFromDataFrame, boolean bToDataFrame) throws IOException, DMLException, ParseException {
    boolean oldConfig = DMLScript.USE_LOCAL_SPARK_CONFIG;
    DMLScript.USE_LOCAL_SPARK_CONFIG = true;
    RUNTIME_PLATFORM oldRT = DMLScript.rtplatform;
    DMLScript.rtplatform = RUNTIME_PLATFORM.HYBRID_SPARK;
    int rowstart = 234, rowend = 1478, colstart = 125, colend = 568;
    int bRows = rowend - rowstart + 1, bCols = colend - colstart + 1;
    int rowstartC = 124, rowendC = 1178, colstartC = 143, colendC = 368;
    int cRows = rowendC - rowstartC + 1, cCols = colendC - colstartC + 1;
    HashMap<String, ValueType[]> outputSchema = new HashMap<String, ValueType[]>();
    HashMap<String, MatrixCharacteristics> outputMC = new HashMap<String, MatrixCharacteristics>();
    TestConfiguration config = getTestConfiguration(TEST_NAME);
    loadTestConfiguration(config);
    List<String> proArgs = new ArrayList<String>();
    proArgs.add(input("A"));
    proArgs.add(Integer.toString(rows));
    proArgs.add(Integer.toString(cols));
    proArgs.add(input("B"));
    proArgs.add(Integer.toString(bRows));
    proArgs.add(Integer.toString(bCols));
    proArgs.add(Integer.toString(rowstart));
    proArgs.add(Integer.toString(rowend));
    proArgs.add(Integer.toString(colstart));
    proArgs.add(Integer.toString(colend));
    proArgs.add(output("A"));
    proArgs.add(Integer.toString(rowstartC));
    proArgs.add(Integer.toString(rowendC));
    proArgs.add(Integer.toString(colstartC));
    proArgs.add(Integer.toString(colendC));
    proArgs.add(output("C"));
    fullDMLScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".dml";
    ValueType[] schema = schemaMixedLarge;
    // initialize the frame data.
    List<ValueType> lschema = Arrays.asList(schema);
    fullRScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".R";
    rCmd = "Rscript" + " " + fullRScriptName + " " + inputDir() + " " + rowstart + " " + rowend + " " + colstart + " " + colend + " " + expectedDir() + " " + rowstartC + " " + rowendC + " " + colstartC + " " + colendC;
    double sparsity = sparsity1;
    double[][] A = getRandomMatrix(rows, cols, min, max, sparsity, 1111);
    writeInputFrameWithMTD("A", A, true, schema, oinfo);
    sparsity = sparsity2;
    double[][] B = getRandomMatrix((int) (bRows), (int) (bCols), min, max, sparsity, 2345);
    ValueType[] schemaB = new ValueType[bCols];
    for (int i = 0; i < bCols; ++i) schemaB[i] = schema[colstart - 1 + i];
    List<ValueType> lschemaB = Arrays.asList(schemaB);
    writeInputFrameWithMTD("B", B, true, schemaB, oinfo);
    ValueType[] schemaC = new ValueType[colendC - colstartC + 1];
    for (int i = 0; i < cCols; ++i) schemaC[i] = schema[colstartC - 1 + i];
    Dataset<Row> dfA = null, dfB = null;
    if (bFromDataFrame) {
        // Create DataFrame for input A
        StructType dfSchemaA = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schema, false);
        JavaRDD<Row> rowRDDA = FrameRDDConverterUtils.csvToRowRDD(sc, input("A"), DataExpression.DEFAULT_DELIM_DELIMITER, schema);
        dfA = spark.createDataFrame(rowRDDA, dfSchemaA);
        // Create DataFrame for input B
        StructType dfSchemaB = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schemaB, false);
        JavaRDD<Row> rowRDDB = FrameRDDConverterUtils.csvToRowRDD(sc, input("B"), DataExpression.DEFAULT_DELIM_DELIMITER, schemaB);
        dfB = spark.createDataFrame(rowRDDB, dfSchemaB);
    }
    try {
        Script script = ScriptFactory.dmlFromFile(fullDMLScriptName);
        String format = "csv";
        if (oinfo == OutputInfo.TextCellOutputInfo)
            format = "text";
        if (bFromDataFrame) {
            script.in("A", dfA);
        } else {
            JavaRDD<String> aIn = sc.textFile(input("A"));
            FrameSchema fs = new FrameSchema(lschema);
            FrameFormat ff = (format.equals("text")) ? FrameFormat.IJV : FrameFormat.CSV;
            FrameMetadata fm = new FrameMetadata(ff, fs, rows, cols);
            script.in("A", aIn, fm);
        }
        if (bFromDataFrame) {
            script.in("B", dfB);
        } else {
            JavaRDD<String> bIn = sc.textFile(input("B"));
            FrameSchema fs = new FrameSchema(lschemaB);
            FrameFormat ff = (format.equals("text")) ? FrameFormat.IJV : FrameFormat.CSV;
            FrameMetadata fm = new FrameMetadata(ff, fs, bRows, bCols);
            script.in("B", bIn, fm);
        }
        // Output one frame to HDFS and get one as RDD //TODO HDFS input/output to do
        script.out("A", "C");
        // set positional argument values
        for (int argNum = 1; argNum <= proArgs.size(); argNum++) {
            script.in("$" + argNum, proArgs.get(argNum - 1));
        }
        MLResults results = ml.execute(script);
        format = "csv";
        if (iinfo == InputInfo.TextCellInputInfo)
            format = "text";
        String fName = output("AB");
        try {
            MapReduceTool.deleteFileIfExistOnHDFS(fName);
        } catch (IOException e) {
            throw new DMLRuntimeException("Error: While deleting file on HDFS");
        }
        if (!bToDataFrame) {
            if (format.equals("text")) {
                JavaRDD<String> javaRDDStringIJV = results.getJavaRDDStringIJV("A");
                javaRDDStringIJV.saveAsTextFile(fName);
            } else {
                JavaRDD<String> javaRDDStringCSV = results.getJavaRDDStringCSV("A");
                javaRDDStringCSV.saveAsTextFile(fName);
            }
        } else {
            Dataset<Row> df = results.getDataFrame("A");
            // Convert back DataFrame to binary block for comparison using original binary to converted DF and back to binary
            MatrixCharacteristics mc = new MatrixCharacteristics(rows, cols, -1, -1, -1);
            JavaPairRDD<LongWritable, FrameBlock> rddOut = FrameRDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc, bFromDataFrame).mapToPair(new LongFrameToLongWritableFrameFunction());
            rddOut.saveAsHadoopFile(output("AB"), LongWritable.class, FrameBlock.class, OutputInfo.BinaryBlockOutputInfo.outputFormatClass);
        }
        fName = output("C");
        try {
            MapReduceTool.deleteFileIfExistOnHDFS(fName);
        } catch (IOException e) {
            throw new DMLRuntimeException("Error: While deleting file on HDFS");
        }
        if (!bToDataFrame) {
            if (format.equals("text")) {
                JavaRDD<String> javaRDDStringIJV = results.getJavaRDDStringIJV("C");
                javaRDDStringIJV.saveAsTextFile(fName);
            } else {
                JavaRDD<String> javaRDDStringCSV = results.getJavaRDDStringCSV("C");
                javaRDDStringCSV.saveAsTextFile(fName);
            }
        } else {
            Dataset<Row> df = results.getDataFrame("C");
            // Convert back DataFrame to binary block for comparison using original binary to converted DF and back to binary
            MatrixCharacteristics mc = new MatrixCharacteristics(cRows, cCols, -1, -1, -1);
            JavaPairRDD<LongWritable, FrameBlock> rddOut = FrameRDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc, bFromDataFrame).mapToPair(new LongFrameToLongWritableFrameFunction());
            rddOut.saveAsHadoopFile(fName, LongWritable.class, FrameBlock.class, OutputInfo.BinaryBlockOutputInfo.outputFormatClass);
        }
        runRScript(true);
        outputSchema.put("AB", schema);
        outputMC.put("AB", new MatrixCharacteristics(rows, cols, -1, -1));
        outputSchema.put("C", schemaC);
        outputMC.put("C", new MatrixCharacteristics(cRows, cCols, -1, -1));
        for (String file : config.getOutputFiles()) {
            MatrixCharacteristics md = outputMC.get(file);
            FrameBlock frameBlock = readDMLFrameFromHDFS(file, iinfo, md);
            FrameBlock frameRBlock = readRFrameFromHDFS(file + ".csv", InputInfo.CSVInputInfo, md);
            ValueType[] schemaOut = outputSchema.get(file);
            verifyFrameData(frameBlock, frameRBlock, schemaOut);
            System.out.println("File " + file + " processed successfully.");
        }
        System.out.println("Frame MLContext test completed successfully.");
    } finally {
        DMLScript.rtplatform = oldRT;
        DMLScript.USE_LOCAL_SPARK_CONFIG = oldConfig;
    }
}

Example 2 with FrameSchema

use of org.apache.sysml.api.mlcontext.FrameSchema in project incubator-systemml by apache.

the class FrameTest method testFrameGeneral.

private void testFrameGeneral(InputInfo iinfo, OutputInfo oinfo, boolean bFromDataFrame, boolean bToDataFrame) throws IOException, DMLException, ParseException {
    boolean oldConfig = DMLScript.USE_LOCAL_SPARK_CONFIG;
    DMLScript.USE_LOCAL_SPARK_CONFIG = true;
    RUNTIME_PLATFORM oldRT = DMLScript.rtplatform;
    DMLScript.rtplatform = RUNTIME_PLATFORM.HYBRID_SPARK;
    int rowstart = 234, rowend = 1478, colstart = 125, colend = 568;
    int bRows = rowend - rowstart + 1, bCols = colend - colstart + 1;
    int rowstartC = 124, rowendC = 1178, colstartC = 143, colendC = 368;
    int cRows = rowendC - rowstartC + 1, cCols = colendC - colstartC + 1;
    HashMap<String, ValueType[]> outputSchema = new HashMap<String, ValueType[]>();
    HashMap<String, MatrixCharacteristics> outputMC = new HashMap<String, MatrixCharacteristics>();
    TestConfiguration config = getTestConfiguration(TEST_NAME);
    loadTestConfiguration(config);
    List<String> proArgs = new ArrayList<String>();
    proArgs.add(input("A"));
    proArgs.add(Integer.toString(rows));
    proArgs.add(Integer.toString(cols));
    proArgs.add(input("B"));
    proArgs.add(Integer.toString(bRows));
    proArgs.add(Integer.toString(bCols));
    proArgs.add(Integer.toString(rowstart));
    proArgs.add(Integer.toString(rowend));
    proArgs.add(Integer.toString(colstart));
    proArgs.add(Integer.toString(colend));
    proArgs.add(output("A"));
    proArgs.add(Integer.toString(rowstartC));
    proArgs.add(Integer.toString(rowendC));
    proArgs.add(Integer.toString(colstartC));
    proArgs.add(Integer.toString(colendC));
    proArgs.add(output("C"));
    fullDMLScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".dml";
    ValueType[] schema = schemaMixedLarge;
    // initialize the frame data.
    List<ValueType> lschema = Arrays.asList(schema);
    fullRScriptName = SCRIPT_DIR + TEST_DIR + TEST_NAME + ".R";
    rCmd = "Rscript" + " " + fullRScriptName + " " + inputDir() + " " + rowstart + " " + rowend + " " + colstart + " " + colend + " " + expectedDir() + " " + rowstartC + " " + rowendC + " " + colstartC + " " + colendC;
    double sparsity = sparsity1;
    double[][] A = getRandomMatrix(rows, cols, min, max, sparsity, 1111);
    writeInputFrameWithMTD("A", A, true, schema, oinfo);
    sparsity = sparsity2;
    double[][] B = getRandomMatrix((int) (bRows), (int) (bCols), min, max, sparsity, 2345);
    ValueType[] schemaB = new ValueType[bCols];
    for (int i = 0; i < bCols; ++i) schemaB[i] = schema[colstart - 1 + i];
    List<ValueType> lschemaB = Arrays.asList(schemaB);
    writeInputFrameWithMTD("B", B, true, schemaB, oinfo);
    ValueType[] schemaC = new ValueType[colendC - colstartC + 1];
    for (int i = 0; i < cCols; ++i) schemaC[i] = schema[colstartC - 1 + i];
    Dataset<Row> dfA = null, dfB = null;
    if (bFromDataFrame) {
        // Create DataFrame for input A
        StructType dfSchemaA = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schema, false);
        JavaRDD<Row> rowRDDA = FrameRDDConverterUtils.csvToRowRDD(sc, input("A"), DataExpression.DEFAULT_DELIM_DELIMITER, schema);
        dfA = spark.createDataFrame(rowRDDA, dfSchemaA);
        // Create DataFrame for input B
        StructType dfSchemaB = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schemaB, false);
        JavaRDD<Row> rowRDDB = FrameRDDConverterUtils.csvToRowRDD(sc, input("B"), DataExpression.DEFAULT_DELIM_DELIMITER, schemaB);
        dfB = spark.createDataFrame(rowRDDB, dfSchemaB);
    }
    try {
        Script script = ScriptFactory.dmlFromFile(fullDMLScriptName);
        String format = "csv";
        if (oinfo == OutputInfo.TextCellOutputInfo)
            format = "text";
        if (bFromDataFrame) {
            script.in("A", dfA);
        } else {
            JavaRDD<String> aIn = sc.textFile(input("A"));
            FrameSchema fs = new FrameSchema(lschema);
            FrameFormat ff = (format.equals("text")) ? FrameFormat.IJV : FrameFormat.CSV;
            FrameMetadata fm = new FrameMetadata(ff, fs, rows, cols);
            script.in("A", aIn, fm);
        }
        if (bFromDataFrame) {
            script.in("B", dfB);
        } else {
            JavaRDD<String> bIn = sc.textFile(input("B"));
            FrameSchema fs = new FrameSchema(lschemaB);
            FrameFormat ff = (format.equals("text")) ? FrameFormat.IJV : FrameFormat.CSV;
            FrameMetadata fm = new FrameMetadata(ff, fs, bRows, bCols);
            script.in("B", bIn, fm);
        }
        // Output one frame to HDFS and get one as RDD //TODO HDFS input/output to do
        script.out("A", "C");
        // set positional argument values
        for (int argNum = 1; argNum <= proArgs.size(); argNum++) {
            script.in("$" + argNum, proArgs.get(argNum - 1));
        }
        MLResults results = ml.execute(script);
        format = "csv";
        if (iinfo == InputInfo.TextCellInputInfo)
            format = "text";
        String fName = output("AB");
        try {
            MapReduceTool.deleteFileIfExistOnHDFS(fName);
        } catch (IOException e) {
            throw new DMLRuntimeException("Error: While deleting file on HDFS");
        }
        if (!bToDataFrame) {
            if (format.equals("text")) {
                JavaRDD<String> javaRDDStringIJV = results.getJavaRDDStringIJV("A");
                javaRDDStringIJV.saveAsTextFile(fName);
            } else {
                JavaRDD<String> javaRDDStringCSV = results.getJavaRDDStringCSV("A");
                javaRDDStringCSV.saveAsTextFile(fName);
            }
        } else {
            Dataset<Row> df = results.getDataFrame("A");
            // Convert back DataFrame to binary block for comparison using original binary to converted DF and back to binary
            MatrixCharacteristics mc = new MatrixCharacteristics(rows, cols, -1, -1, -1);
            JavaPairRDD<LongWritable, FrameBlock> rddOut = FrameRDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc, bFromDataFrame).mapToPair(new LongFrameToLongWritableFrameFunction());
            rddOut.saveAsHadoopFile(output("AB"), LongWritable.class, FrameBlock.class, OutputInfo.BinaryBlockOutputInfo.outputFormatClass);
        }
        fName = output("C");
        try {
            MapReduceTool.deleteFileIfExistOnHDFS(fName);
        } catch (IOException e) {
            throw new DMLRuntimeException("Error: While deleting file on HDFS");
        }
        if (!bToDataFrame) {
            if (format.equals("text")) {
                JavaRDD<String> javaRDDStringIJV = results.getJavaRDDStringIJV("C");
                javaRDDStringIJV.saveAsTextFile(fName);
            } else {
                JavaRDD<String> javaRDDStringCSV = results.getJavaRDDStringCSV("C");
                javaRDDStringCSV.saveAsTextFile(fName);
            }
        } else {
            Dataset<Row> df = results.getDataFrame("C");
            // Convert back DataFrame to binary block for comparison using original binary to converted DF and back to binary
            MatrixCharacteristics mc = new MatrixCharacteristics(cRows, cCols, -1, -1, -1);
            JavaPairRDD<LongWritable, FrameBlock> rddOut = FrameRDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc, bFromDataFrame).mapToPair(new LongFrameToLongWritableFrameFunction());
            rddOut.saveAsHadoopFile(fName, LongWritable.class, FrameBlock.class, OutputInfo.BinaryBlockOutputInfo.outputFormatClass);
        }
        runRScript(true);
        outputSchema.put("AB", schema);
        outputMC.put("AB", new MatrixCharacteristics(rows, cols, -1, -1));
        outputSchema.put("C", schemaC);
        outputMC.put("C", new MatrixCharacteristics(cRows, cCols, -1, -1));
        for (String file : config.getOutputFiles()) {
            MatrixCharacteristics md = outputMC.get(file);
            FrameBlock frameBlock = readDMLFrameFromHDFS(file, iinfo, md);
            FrameBlock frameRBlock = readRFrameFromHDFS(file + ".csv", InputInfo.CSVInputInfo, md);
            ValueType[] schemaOut = outputSchema.get(file);
            verifyFrameData(frameBlock, frameRBlock, schemaOut);
            System.out.println("File " + file + " processed successfully.");
        }
        System.out.println("Frame MLContext test completed successfully.");
    } finally {
        DMLScript.rtplatform = oldRT;
        DMLScript.USE_LOCAL_SPARK_CONFIG = oldConfig;
    }
}

Example 3 with FrameSchema

use of org.apache.sysml.api.mlcontext.FrameSchema in project incubator-systemml by apache.

the class MLContextFrameTest method testFrame.

public void testFrame(FrameFormat format, SCRIPT_TYPE script_type, IO_TYPE inputType, IO_TYPE outputType) {
    System.out.println("MLContextTest - Frame JavaRDD<String> for format: " + format + " Script: " + script_type);
    List<String> listA = new ArrayList<String>();
    List<String> listB = new ArrayList<String>();
    FrameMetadata fmA = null, fmB = null;
    Script script = null;
    ValueType[] schemaA = { ValueType.INT, ValueType.STRING, ValueType.DOUBLE, ValueType.BOOLEAN };
    List<ValueType> lschemaA = Arrays.asList(schemaA);
    FrameSchema fschemaA = new FrameSchema(lschemaA);
    ValueType[] schemaB = { ValueType.STRING, ValueType.DOUBLE, ValueType.BOOLEAN };
    List<ValueType> lschemaB = Arrays.asList(schemaB);
    FrameSchema fschemaB = new FrameSchema(lschemaB);
    if (inputType != IO_TYPE.FILE) {
        if (format == FrameFormat.CSV) {
            listA.add("1,Str2,3.0,true");
            listA.add("4,Str5,6.0,false");
            listA.add("7,Str8,9.0,true");
            listB.add("Str12,13.0,true");
            listB.add("Str25,26.0,false");
            fmA = new FrameMetadata(FrameFormat.CSV, fschemaA, 3, 4);
            fmB = new FrameMetadata(FrameFormat.CSV, fschemaB, 2, 3);
        } else if (format == FrameFormat.IJV) {
            listA.add("1 1 1");
            listA.add("1 2 Str2");
            listA.add("1 3 3.0");
            listA.add("1 4 true");
            listA.add("2 1 4");
            listA.add("2 2 Str5");
            listA.add("2 3 6.0");
            listA.add("2 4 false");
            listA.add("3 1 7");
            listA.add("3 2 Str8");
            listA.add("3 3 9.0");
            listA.add("3 4 true");
            listB.add("1 1 Str12");
            listB.add("1 2 13.0");
            listB.add("1 3 true");
            listB.add("2 1 Str25");
            listB.add("2 2 26.0");
            listB.add("2 3 false");
            fmA = new FrameMetadata(FrameFormat.IJV, fschemaA, 3, 4);
            fmB = new FrameMetadata(FrameFormat.IJV, fschemaB, 2, 3);
        }
        JavaRDD<String> javaRDDA = sc.parallelize(listA);
        JavaRDD<String> javaRDDB = sc.parallelize(listB);
        if (inputType == IO_TYPE.DATAFRAME) {
            JavaRDD<Row> javaRddRowA = FrameRDDConverterUtils.csvToRowRDD(sc, javaRDDA, CSV_DELIM, schemaA);
            JavaRDD<Row> javaRddRowB = FrameRDDConverterUtils.csvToRowRDD(sc, javaRDDB, CSV_DELIM, schemaB);
            // Create DataFrame
            StructType dfSchemaA = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schemaA, false);
            Dataset<Row> dataFrameA = spark.createDataFrame(javaRddRowA, dfSchemaA);
            StructType dfSchemaB = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schemaB, false);
            Dataset<Row> dataFrameB = spark.createDataFrame(javaRddRowB, dfSchemaB);
            if (script_type == SCRIPT_TYPE.DML)
                script = dml("A[2:3,2:4]=B;C=A[2:3,2:3]").in("A", dataFrameA, fmA).in("B", dataFrameB, fmB).out("A").out("C");
            else if (script_type == SCRIPT_TYPE.PYDML)
                // DO NOT USE ; at the end of any statment, it throws NPE
                script = pydml("A[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("A", dataFrameA, fmA).in("B", dataFrameB, fmB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
        } else {
            if (inputType == IO_TYPE.JAVA_RDD_STR_CSV || inputType == IO_TYPE.JAVA_RDD_STR_IJV) {
                if (script_type == SCRIPT_TYPE.DML)
                    script = dml("A[2:3,2:4]=B;C=A[2:3,2:3]").in("A", javaRDDA, fmA).in("B", javaRDDB, fmB).out("A").out("C");
                else if (script_type == SCRIPT_TYPE.PYDML)
                    // DO NOT USE ; at the end of any statment, it throws
                    // NPE
                    script = pydml("A[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("A", javaRDDA, fmA).in("B", javaRDDB, fmB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
            } else if (inputType == IO_TYPE.RDD_STR_CSV || inputType == IO_TYPE.RDD_STR_IJV) {
                RDD<String> rddA = JavaRDD.toRDD(javaRDDA);
                RDD<String> rddB = JavaRDD.toRDD(javaRDDB);
                if (script_type == SCRIPT_TYPE.DML)
                    script = dml("A[2:3,2:4]=B;C=A[2:3,2:3]").in("A", rddA, fmA).in("B", rddB, fmB).out("A").out("C");
                else if (script_type == SCRIPT_TYPE.PYDML)
                    // DO NOT USE ; at the end of any statment, it throws
                    // NPE
                    script = pydml("A[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("A", rddA, fmA).in("B", rddB, fmB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
            }
        }
    } else {
        // Input type is file
        String fileA = null, fileB = null;
        if (format == FrameFormat.CSV) {
            fileA = baseDirectory + File.separator + "FrameA.csv";
            fileB = baseDirectory + File.separator + "FrameB.csv";
        } else if (format == FrameFormat.IJV) {
            fileA = baseDirectory + File.separator + "FrameA.ijv";
            fileB = baseDirectory + File.separator + "FrameB.ijv";
        }
        if (script_type == SCRIPT_TYPE.DML)
            script = dml("A=read($A); B=read($B);A[2:3,2:4]=B;C=A[2:3,2:3];A[1,1]=234").in("$A", fileA, fmA).in("$B", fileB, fmB).out("A").out("C");
        else if (script_type == SCRIPT_TYPE.PYDML)
            // DO NOT USE ; at the end of any statment, it throws NPE
            script = pydml("A=load($A)\nB=load($B)\nA[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("$A", fileA).in("$B", fileB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
    }
    MLResults mlResults = ml.execute(script);
    // Validate output schema
    List<ValueType> lschemaOutA = Arrays.asList(mlResults.getFrameObject("A").getSchema());
    List<ValueType> lschemaOutC = Arrays.asList(mlResults.getFrameObject("C").getSchema());
    Assert.assertEquals(ValueType.INT, lschemaOutA.get(0));
    Assert.assertEquals(ValueType.STRING, lschemaOutA.get(1));
    Assert.assertEquals(ValueType.DOUBLE, lschemaOutA.get(2));
    Assert.assertEquals(ValueType.BOOLEAN, lschemaOutA.get(3));
    Assert.assertEquals(ValueType.STRING, lschemaOutC.get(0));
    Assert.assertEquals(ValueType.DOUBLE, lschemaOutC.get(1));
    if (outputType == IO_TYPE.JAVA_RDD_STR_CSV) {
        JavaRDD<String> javaRDDStringCSVA = mlResults.getJavaRDDStringCSV("A");
        List<String> linesA = javaRDDStringCSVA.collect();
        Assert.assertEquals("1,Str2,3.0,true", linesA.get(0));
        Assert.assertEquals("4,Str12,13.0,true", linesA.get(1));
        Assert.assertEquals("7,Str25,26.0,false", linesA.get(2));
        JavaRDD<String> javaRDDStringCSVC = mlResults.getJavaRDDStringCSV("C");
        List<String> linesC = javaRDDStringCSVC.collect();
        Assert.assertEquals("Str12,13.0", linesC.get(0));
        Assert.assertEquals("Str25,26.0", linesC.get(1));
    } else if (outputType == IO_TYPE.JAVA_RDD_STR_IJV) {
        JavaRDD<String> javaRDDStringIJVA = mlResults.getJavaRDDStringIJV("A");
        List<String> linesA = javaRDDStringIJVA.collect();
        Assert.assertEquals("1 1 1", linesA.get(0));
        Assert.assertEquals("1 2 Str2", linesA.get(1));
        Assert.assertEquals("1 3 3.0", linesA.get(2));
        Assert.assertEquals("1 4 true", linesA.get(3));
        Assert.assertEquals("2 1 4", linesA.get(4));
        Assert.assertEquals("2 2 Str12", linesA.get(5));
        Assert.assertEquals("2 3 13.0", linesA.get(6));
        Assert.assertEquals("2 4 true", linesA.get(7));
        JavaRDD<String> javaRDDStringIJVC = mlResults.getJavaRDDStringIJV("C");
        List<String> linesC = javaRDDStringIJVC.collect();
        Assert.assertEquals("1 1 Str12", linesC.get(0));
        Assert.assertEquals("1 2 13.0", linesC.get(1));
        Assert.assertEquals("2 1 Str25", linesC.get(2));
        Assert.assertEquals("2 2 26.0", linesC.get(3));
    } else if (outputType == IO_TYPE.RDD_STR_CSV) {
        RDD<String> rddStringCSVA = mlResults.getRDDStringCSV("A");
        Iterator<String> iteratorA = rddStringCSVA.toLocalIterator();
        Assert.assertEquals("1,Str2,3.0,true", iteratorA.next());
        Assert.assertEquals("4,Str12,13.0,true", iteratorA.next());
        Assert.assertEquals("7,Str25,26.0,false", iteratorA.next());
        RDD<String> rddStringCSVC = mlResults.getRDDStringCSV("C");
        Iterator<String> iteratorC = rddStringCSVC.toLocalIterator();
        Assert.assertEquals("Str12,13.0", iteratorC.next());
        Assert.assertEquals("Str25,26.0", iteratorC.next());
    } else if (outputType == IO_TYPE.RDD_STR_IJV) {
        RDD<String> rddStringIJVA = mlResults.getRDDStringIJV("A");
        Iterator<String> iteratorA = rddStringIJVA.toLocalIterator();
        Assert.assertEquals("1 1 1", iteratorA.next());
        Assert.assertEquals("1 2 Str2", iteratorA.next());
        Assert.assertEquals("1 3 3.0", iteratorA.next());
        Assert.assertEquals("1 4 true", iteratorA.next());
        Assert.assertEquals("2 1 4", iteratorA.next());
        Assert.assertEquals("2 2 Str12", iteratorA.next());
        Assert.assertEquals("2 3 13.0", iteratorA.next());
        Assert.assertEquals("2 4 true", iteratorA.next());
        Assert.assertEquals("3 1 7", iteratorA.next());
        Assert.assertEquals("3 2 Str25", iteratorA.next());
        Assert.assertEquals("3 3 26.0", iteratorA.next());
        Assert.assertEquals("3 4 false", iteratorA.next());
        RDD<String> rddStringIJVC = mlResults.getRDDStringIJV("C");
        Iterator<String> iteratorC = rddStringIJVC.toLocalIterator();
        Assert.assertEquals("1 1 Str12", iteratorC.next());
        Assert.assertEquals("1 2 13.0", iteratorC.next());
        Assert.assertEquals("2 1 Str25", iteratorC.next());
        Assert.assertEquals("2 2 26.0", iteratorC.next());
    } else if (outputType == IO_TYPE.DATAFRAME) {
        Dataset<Row> dataFrameA = mlResults.getDataFrame("A").drop(RDDConverterUtils.DF_ID_COLUMN);
        StructType dfschemaA = dataFrameA.schema();
        StructField structTypeA = dfschemaA.apply(0);
        Assert.assertEquals(DataTypes.LongType, structTypeA.dataType());
        structTypeA = dfschemaA.apply(1);
        Assert.assertEquals(DataTypes.StringType, structTypeA.dataType());
        structTypeA = dfschemaA.apply(2);
        Assert.assertEquals(DataTypes.DoubleType, structTypeA.dataType());
        structTypeA = dfschemaA.apply(3);
        Assert.assertEquals(DataTypes.BooleanType, structTypeA.dataType());
        List<Row> listAOut = dataFrameA.collectAsList();
        Row row1 = listAOut.get(0);
        Assert.assertEquals("Mismatch with expected value", Long.valueOf(1), row1.get(0));
        Assert.assertEquals("Mismatch with expected value", "Str2", row1.get(1));
        Assert.assertEquals("Mismatch with expected value", 3.0, row1.get(2));
        Assert.assertEquals("Mismatch with expected value", true, row1.get(3));
        Row row2 = listAOut.get(1);
        Assert.assertEquals("Mismatch with expected value", Long.valueOf(4), row2.get(0));
        Assert.assertEquals("Mismatch with expected value", "Str12", row2.get(1));
        Assert.assertEquals("Mismatch with expected value", 13.0, row2.get(2));
        Assert.assertEquals("Mismatch with expected value", true, row2.get(3));
        Dataset<Row> dataFrameC = mlResults.getDataFrame("C").drop(RDDConverterUtils.DF_ID_COLUMN);
        StructType dfschemaC = dataFrameC.schema();
        StructField structTypeC = dfschemaC.apply(0);
        Assert.assertEquals(DataTypes.StringType, structTypeC.dataType());
        structTypeC = dfschemaC.apply(1);
        Assert.assertEquals(DataTypes.DoubleType, structTypeC.dataType());
        List<Row> listCOut = dataFrameC.collectAsList();
        Row row3 = listCOut.get(0);
        Assert.assertEquals("Mismatch with expected value", "Str12", row3.get(0));
        Assert.assertEquals("Mismatch with expected value", 13.0, row3.get(1));
        Row row4 = listCOut.get(1);
        Assert.assertEquals("Mismatch with expected value", "Str25", row4.get(0));
        Assert.assertEquals("Mismatch with expected value", 26.0, row4.get(1));
    } else {
        String[][] frameA = mlResults.getFrameAs2DStringArray("A");
        Assert.assertEquals("Str2", frameA[0][1]);
        Assert.assertEquals("3.0", frameA[0][2]);
        Assert.assertEquals("13.0", frameA[1][2]);
        Assert.assertEquals("true", frameA[1][3]);
        Assert.assertEquals("Str25", frameA[2][1]);
        String[][] frameC = mlResults.getFrameAs2DStringArray("C");
        Assert.assertEquals("Str12", frameC[0][0]);
        Assert.assertEquals("Str25", frameC[1][0]);
        Assert.assertEquals("13.0", frameC[0][1]);
        Assert.assertEquals("26.0", frameC[1][1]);
    }
}

Example 4 with FrameSchema

use of org.apache.sysml.api.mlcontext.FrameSchema in project systemml by apache.

the class MLContextFrameTest method testFrame.

public void testFrame(FrameFormat format, SCRIPT_TYPE script_type, IO_TYPE inputType, IO_TYPE outputType) {
    System.out.println("MLContextTest - Frame JavaRDD<String> for format: " + format + " Script: " + script_type);
    List<String> listA = new ArrayList<String>();
    List<String> listB = new ArrayList<String>();
    FrameMetadata fmA = null, fmB = null;
    Script script = null;
    ValueType[] schemaA = { ValueType.INT, ValueType.STRING, ValueType.DOUBLE, ValueType.BOOLEAN };
    List<ValueType> lschemaA = Arrays.asList(schemaA);
    FrameSchema fschemaA = new FrameSchema(lschemaA);
    ValueType[] schemaB = { ValueType.STRING, ValueType.DOUBLE, ValueType.BOOLEAN };
    List<ValueType> lschemaB = Arrays.asList(schemaB);
    FrameSchema fschemaB = new FrameSchema(lschemaB);
    if (inputType != IO_TYPE.FILE) {
        if (format == FrameFormat.CSV) {
            listA.add("1,Str2,3.0,true");
            listA.add("4,Str5,6.0,false");
            listA.add("7,Str8,9.0,true");
            listB.add("Str12,13.0,true");
            listB.add("Str25,26.0,false");
            fmA = new FrameMetadata(FrameFormat.CSV, fschemaA, 3, 4);
            fmB = new FrameMetadata(FrameFormat.CSV, fschemaB, 2, 3);
        } else if (format == FrameFormat.IJV) {
            listA.add("1 1 1");
            listA.add("1 2 Str2");
            listA.add("1 3 3.0");
            listA.add("1 4 true");
            listA.add("2 1 4");
            listA.add("2 2 Str5");
            listA.add("2 3 6.0");
            listA.add("2 4 false");
            listA.add("3 1 7");
            listA.add("3 2 Str8");
            listA.add("3 3 9.0");
            listA.add("3 4 true");
            listB.add("1 1 Str12");
            listB.add("1 2 13.0");
            listB.add("1 3 true");
            listB.add("2 1 Str25");
            listB.add("2 2 26.0");
            listB.add("2 3 false");
            fmA = new FrameMetadata(FrameFormat.IJV, fschemaA, 3, 4);
            fmB = new FrameMetadata(FrameFormat.IJV, fschemaB, 2, 3);
        }
        JavaRDD<String> javaRDDA = sc.parallelize(listA);
        JavaRDD<String> javaRDDB = sc.parallelize(listB);
        if (inputType == IO_TYPE.DATAFRAME) {
            JavaRDD<Row> javaRddRowA = FrameRDDConverterUtils.csvToRowRDD(sc, javaRDDA, CSV_DELIM, schemaA);
            JavaRDD<Row> javaRddRowB = FrameRDDConverterUtils.csvToRowRDD(sc, javaRDDB, CSV_DELIM, schemaB);
            // Create DataFrame
            StructType dfSchemaA = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schemaA, false);
            Dataset<Row> dataFrameA = spark.createDataFrame(javaRddRowA, dfSchemaA);
            StructType dfSchemaB = FrameRDDConverterUtils.convertFrameSchemaToDFSchema(schemaB, false);
            Dataset<Row> dataFrameB = spark.createDataFrame(javaRddRowB, dfSchemaB);
            if (script_type == SCRIPT_TYPE.DML)
                script = dml("A[2:3,2:4]=B;C=A[2:3,2:3]").in("A", dataFrameA, fmA).in("B", dataFrameB, fmB).out("A").out("C");
            else if (script_type == SCRIPT_TYPE.PYDML)
                // DO NOT USE ; at the end of any statment, it throws NPE
                script = pydml("A[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("A", dataFrameA, fmA).in("B", dataFrameB, fmB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
        } else {
            if (inputType == IO_TYPE.JAVA_RDD_STR_CSV || inputType == IO_TYPE.JAVA_RDD_STR_IJV) {
                if (script_type == SCRIPT_TYPE.DML)
                    script = dml("A[2:3,2:4]=B;C=A[2:3,2:3]").in("A", javaRDDA, fmA).in("B", javaRDDB, fmB).out("A").out("C");
                else if (script_type == SCRIPT_TYPE.PYDML)
                    // DO NOT USE ; at the end of any statment, it throws
                    // NPE
                    script = pydml("A[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("A", javaRDDA, fmA).in("B", javaRDDB, fmB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
            } else if (inputType == IO_TYPE.RDD_STR_CSV || inputType == IO_TYPE.RDD_STR_IJV) {
                RDD<String> rddA = JavaRDD.toRDD(javaRDDA);
                RDD<String> rddB = JavaRDD.toRDD(javaRDDB);
                if (script_type == SCRIPT_TYPE.DML)
                    script = dml("A[2:3,2:4]=B;C=A[2:3,2:3]").in("A", rddA, fmA).in("B", rddB, fmB).out("A").out("C");
                else if (script_type == SCRIPT_TYPE.PYDML)
                    // DO NOT USE ; at the end of any statment, it throws
                    // NPE
                    script = pydml("A[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("A", rddA, fmA).in("B", rddB, fmB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
            }
        }
    } else {
        // Input type is file
        String fileA = null, fileB = null;
        if (format == FrameFormat.CSV) {
            fileA = baseDirectory + File.separator + "FrameA.csv";
            fileB = baseDirectory + File.separator + "FrameB.csv";
        } else if (format == FrameFormat.IJV) {
            fileA = baseDirectory + File.separator + "FrameA.ijv";
            fileB = baseDirectory + File.separator + "FrameB.ijv";
        }
        if (script_type == SCRIPT_TYPE.DML)
            script = dml("A=read($A); B=read($B);A[2:3,2:4]=B;C=A[2:3,2:3];A[1,1]=234").in("$A", fileA, fmA).in("$B", fileB, fmB).out("A").out("C");
        else if (script_type == SCRIPT_TYPE.PYDML)
            // DO NOT USE ; at the end of any statment, it throws NPE
            script = pydml("A=load($A)\nB=load($B)\nA[$X:$Y,$X:$Z]=B\nC=A[$X:$Y,$X:$Y]").in("$A", fileA).in("$B", fileB).in("$X", 1).in("$Y", 3).in("$Z", 4).out("A").out("C");
    }
    MLResults mlResults = ml.execute(script);
    // Validate output schema
    List<ValueType> lschemaOutA = Arrays.asList(mlResults.getFrameObject("A").getSchema());
    List<ValueType> lschemaOutC = Arrays.asList(mlResults.getFrameObject("C").getSchema());
    Assert.assertEquals(ValueType.INT, lschemaOutA.get(0));
    Assert.assertEquals(ValueType.STRING, lschemaOutA.get(1));
    Assert.assertEquals(ValueType.DOUBLE, lschemaOutA.get(2));
    Assert.assertEquals(ValueType.BOOLEAN, lschemaOutA.get(3));
    Assert.assertEquals(ValueType.STRING, lschemaOutC.get(0));
    Assert.assertEquals(ValueType.DOUBLE, lschemaOutC.get(1));
    if (outputType == IO_TYPE.JAVA_RDD_STR_CSV) {
        JavaRDD<String> javaRDDStringCSVA = mlResults.getJavaRDDStringCSV("A");
        List<String> linesA = javaRDDStringCSVA.collect();
        Assert.assertEquals("1,Str2,3.0,true", linesA.get(0));
        Assert.assertEquals("4,Str12,13.0,true", linesA.get(1));
        Assert.assertEquals("7,Str25,26.0,false", linesA.get(2));
        JavaRDD<String> javaRDDStringCSVC = mlResults.getJavaRDDStringCSV("C");
        List<String> linesC = javaRDDStringCSVC.collect();
        Assert.assertEquals("Str12,13.0", linesC.get(0));
        Assert.assertEquals("Str25,26.0", linesC.get(1));
    } else if (outputType == IO_TYPE.JAVA_RDD_STR_IJV) {
        JavaRDD<String> javaRDDStringIJVA = mlResults.getJavaRDDStringIJV("A");
        List<String> linesA = javaRDDStringIJVA.collect();
        Assert.assertEquals("1 1 1", linesA.get(0));
        Assert.assertEquals("1 2 Str2", linesA.get(1));
        Assert.assertEquals("1 3 3.0", linesA.get(2));
        Assert.assertEquals("1 4 true", linesA.get(3));
        Assert.assertEquals("2 1 4", linesA.get(4));
        Assert.assertEquals("2 2 Str12", linesA.get(5));
        Assert.assertEquals("2 3 13.0", linesA.get(6));
        Assert.assertEquals("2 4 true", linesA.get(7));
        JavaRDD<String> javaRDDStringIJVC = mlResults.getJavaRDDStringIJV("C");
        List<String> linesC = javaRDDStringIJVC.collect();
        Assert.assertEquals("1 1 Str12", linesC.get(0));
        Assert.assertEquals("1 2 13.0", linesC.get(1));
        Assert.assertEquals("2 1 Str25", linesC.get(2));
        Assert.assertEquals("2 2 26.0", linesC.get(3));
    } else if (outputType == IO_TYPE.RDD_STR_CSV) {
        RDD<String> rddStringCSVA = mlResults.getRDDStringCSV("A");
        Iterator<String> iteratorA = rddStringCSVA.toLocalIterator();
        Assert.assertEquals("1,Str2,3.0,true", iteratorA.next());
        Assert.assertEquals("4,Str12,13.0,true", iteratorA.next());
        Assert.assertEquals("7,Str25,26.0,false", iteratorA.next());
        RDD<String> rddStringCSVC = mlResults.getRDDStringCSV("C");
        Iterator<String> iteratorC = rddStringCSVC.toLocalIterator();
        Assert.assertEquals("Str12,13.0", iteratorC.next());
        Assert.assertEquals("Str25,26.0", iteratorC.next());
    } else if (outputType == IO_TYPE.RDD_STR_IJV) {
        RDD<String> rddStringIJVA = mlResults.getRDDStringIJV("A");
        Iterator<String> iteratorA = rddStringIJVA.toLocalIterator();
        Assert.assertEquals("1 1 1", iteratorA.next());
        Assert.assertEquals("1 2 Str2", iteratorA.next());
        Assert.assertEquals("1 3 3.0", iteratorA.next());
        Assert.assertEquals("1 4 true", iteratorA.next());
        Assert.assertEquals("2 1 4", iteratorA.next());
        Assert.assertEquals("2 2 Str12", iteratorA.next());
        Assert.assertEquals("2 3 13.0", iteratorA.next());
        Assert.assertEquals("2 4 true", iteratorA.next());
        Assert.assertEquals("3 1 7", iteratorA.next());
        Assert.assertEquals("3 2 Str25", iteratorA.next());
        Assert.assertEquals("3 3 26.0", iteratorA.next());
        Assert.assertEquals("3 4 false", iteratorA.next());
        RDD<String> rddStringIJVC = mlResults.getRDDStringIJV("C");
        Iterator<String> iteratorC = rddStringIJVC.toLocalIterator();
        Assert.assertEquals("1 1 Str12", iteratorC.next());
        Assert.assertEquals("1 2 13.0", iteratorC.next());
        Assert.assertEquals("2 1 Str25", iteratorC.next());
        Assert.assertEquals("2 2 26.0", iteratorC.next());
    } else if (outputType == IO_TYPE.DATAFRAME) {
        Dataset<Row> dataFrameA = mlResults.getDataFrame("A").drop(RDDConverterUtils.DF_ID_COLUMN);
        StructType dfschemaA = dataFrameA.schema();
        StructField structTypeA = dfschemaA.apply(0);
        Assert.assertEquals(DataTypes.LongType, structTypeA.dataType());
        structTypeA = dfschemaA.apply(1);
        Assert.assertEquals(DataTypes.StringType, structTypeA.dataType());
        structTypeA = dfschemaA.apply(2);
        Assert.assertEquals(DataTypes.DoubleType, structTypeA.dataType());
        structTypeA = dfschemaA.apply(3);
        Assert.assertEquals(DataTypes.BooleanType, structTypeA.dataType());
        List<Row> listAOut = dataFrameA.collectAsList();
        Row row1 = listAOut.get(0);
        Assert.assertEquals("Mismatch with expected value", Long.valueOf(1), row1.get(0));
        Assert.assertEquals("Mismatch with expected value", "Str2", row1.get(1));
        Assert.assertEquals("Mismatch with expected value", 3.0, row1.get(2));
        Assert.assertEquals("Mismatch with expected value", true, row1.get(3));
        Row row2 = listAOut.get(1);
        Assert.assertEquals("Mismatch with expected value", Long.valueOf(4), row2.get(0));
        Assert.assertEquals("Mismatch with expected value", "Str12", row2.get(1));
        Assert.assertEquals("Mismatch with expected value", 13.0, row2.get(2));
        Assert.assertEquals("Mismatch with expected value", true, row2.get(3));
        Dataset<Row> dataFrameC = mlResults.getDataFrame("C").drop(RDDConverterUtils.DF_ID_COLUMN);
        StructType dfschemaC = dataFrameC.schema();
        StructField structTypeC = dfschemaC.apply(0);
        Assert.assertEquals(DataTypes.StringType, structTypeC.dataType());
        structTypeC = dfschemaC.apply(1);
        Assert.assertEquals(DataTypes.DoubleType, structTypeC.dataType());
        List<Row> listCOut = dataFrameC.collectAsList();
        Row row3 = listCOut.get(0);
        Assert.assertEquals("Mismatch with expected value", "Str12", row3.get(0));
        Assert.assertEquals("Mismatch with expected value", 13.0, row3.get(1));
        Row row4 = listCOut.get(1);
        Assert.assertEquals("Mismatch with expected value", "Str25", row4.get(0));
        Assert.assertEquals("Mismatch with expected value", 26.0, row4.get(1));
    } else {
        String[][] frameA = mlResults.getFrameAs2DStringArray("A");
        Assert.assertEquals("Str2", frameA[0][1]);
        Assert.assertEquals("3.0", frameA[0][2]);
        Assert.assertEquals("13.0", frameA[1][2]);
        Assert.assertEquals("true", frameA[1][3]);
        Assert.assertEquals("Str25", frameA[2][1]);
        String[][] frameC = mlResults.getFrameAs2DStringArray("C");
        Assert.assertEquals("Str12", frameC[0][0]);
        Assert.assertEquals("Str25", frameC[1][0]);
        Assert.assertEquals("13.0", frameC[0][1]);
        Assert.assertEquals("26.0", frameC[1][1]);
    }
}