Examples with ResultVariables org.apache.sysml.api.jmlc.ResultVariables used on opensource projects

Example 21 with ResultVariables

use of org.apache.sysml.api.jmlc.ResultVariables in project incubator-systemml by apache.

the class BuildLiteExecution method jmlcLinReg.

public static void jmlcLinReg() throws Exception {
    Connection conn = getConfiguredConnection();
    String linRegDS = conn.readScript("scripts/algorithms/LinearRegDS.dml");
    PreparedScript linRegDSScript = conn.prepareScript(linRegDS, new String[] { "X", "y" }, new String[] { "beta_out" }, false);
    double[][] trainData = new double[500][3];
    for (int i = 0; i < 500; i++) {
        double one = ThreadLocalRandom.current().nextDouble(0, 100);
        double two = ThreadLocalRandom.current().nextDouble(0, 100);
        double three = ThreadLocalRandom.current().nextDouble(0, 100);
        double[] row = new double[] { one, two, three };
        trainData[i] = row;
    }
    linRegDSScript.setMatrix("X", trainData);
    log.debug(displayMatrix(trainData));
    double[][] trainLabels = new double[500][1];
    for (int i = 0; i < 500; i++) {
        double one = ThreadLocalRandom.current().nextDouble(0, 100);
        double[] row = new double[] { one };
        trainLabels[i] = row;
    }
    linRegDSScript.setMatrix("y", trainLabels);
    log.debug(displayMatrix(trainLabels));
    ResultVariables linRegDSResults = linRegDSScript.executeScript();
    double[][] dsBetas = linRegDSResults.getMatrix("beta_out");
    log.debug("DS BETAS:");
    log.debug(displayMatrix(dsBetas));
    String linRegCG = conn.readScript("scripts/algorithms/LinearRegCG.dml");
    PreparedScript linRegCGScript = conn.prepareScript(linRegCG, new String[] { "X", "y" }, new String[] { "beta_out" }, false);
    linRegCGScript.setMatrix("X", trainData);
    linRegCGScript.setMatrix("y", trainLabels);
    ResultVariables linRegCGResults = linRegCGScript.executeScript();
    double[][] cgBetas = linRegCGResults.getMatrix("beta_out");
    log.debug("CG BETAS:");
    log.debug(displayMatrix(cgBetas));
    String glmPredict = conn.readScript("scripts/algorithms/GLM-predict.dml");
    PreparedScript glmPredictScript = conn.prepareScript(glmPredict, new String[] { "X", "Y", "B_full" }, new String[] { "means" }, false);
    double[][] testData = new double[500][3];
    for (int i = 0; i < 500; i++) {
        double one = ThreadLocalRandom.current().nextDouble(0, 100);
        double two = ThreadLocalRandom.current().nextDouble(0, 100);
        double three = ThreadLocalRandom.current().nextDouble(0, 100);
        double[] row = new double[] { one, two, three };
        testData[i] = row;
    }
    glmPredictScript.setMatrix("X", testData);
    double[][] testLabels = new double[500][1];
    for (int i = 0; i < 500; i++) {
        double one = ThreadLocalRandom.current().nextDouble(0, 100);
        double[] row = new double[] { one };
        testLabels[i] = row;
    }
    glmPredictScript.setMatrix("Y", testLabels);
    glmPredictScript.setMatrix("B_full", cgBetas);
    ResultVariables glmPredictResults = glmPredictScript.executeScript();
    double[][] means = glmPredictResults.getMatrix("means");
    log.debug("GLM PREDICT MEANS:");
    log.debug(displayMatrix(means));
    conn.close();
}

Example 22 with ResultVariables

use of org.apache.sysml.api.jmlc.ResultVariables in project systemml by apache.

the class BuildLiteExecution method jmlcL2SVM.

public static void jmlcL2SVM() throws Exception {
    Connection conn = getConfiguredConnection();
    String dml = conn.readScript("scripts/algorithms/l2-svm.dml");
    PreparedScript l2svm = conn.prepareScript(dml, new String[] { "X", "Y", "fmt", "Log" }, new String[] { "w", "debug_str" }, false);
    double[][] trainData = new double[150][3];
    for (int i = 0; i < 150; i++) {
        int one = ThreadLocalRandom.current().nextInt(0, 101);
        int two = ThreadLocalRandom.current().nextInt(0, 101);
        int three = ThreadLocalRandom.current().nextInt(0, 101);
        double[] row = new double[] { one, two, three };
        trainData[i] = row;
    }
    l2svm.setMatrix("X", trainData);
    log.debug(displayMatrix(trainData));
    double[][] trainLabels = new double[150][1];
    for (int i = 0; i < 150; i++) {
        int one = ThreadLocalRandom.current().nextInt(1, 3);
        double[] row = new double[] { one };
        trainLabels[i] = row;
    }
    l2svm.setMatrix("Y", trainLabels);
    log.debug(displayMatrix(trainLabels));
    l2svm.setScalar("fmt", "csv");
    l2svm.setScalar("Log", "temp/l2-svm-log.csv");
    ResultVariables l2svmResults = l2svm.executeScript();
    double[][] model = l2svmResults.getMatrix("w");
    log.debug("MODEL:");
    log.debug(displayMatrix(model));
    String debugString = l2svmResults.getString("debug_str");
    log.debug("DEBUG STRING:");
    log.debug(debugString);
    String s = conn.readScript("scripts/algorithms/l2-svm-predict.dml");
    Map<String, String> m = new HashMap<>();
    m.put("$Y", "temp/1.csv");
    m.put("$confusion", "temp/2.csv");
    m.put("$scores", "temp/3.csv");
    PreparedScript l2svmPredict = conn.prepareScript(s, m, new String[] { "X", "Y", "w", "fmt" }, new String[] { "scores", "confusion_mat" }, false);
    double[][] testData = new double[150][3];
    for (int i = 0; i < 150; i++) {
        int one = ThreadLocalRandom.current().nextInt(0, 101);
        int two = ThreadLocalRandom.current().nextInt(0, 101);
        int three = ThreadLocalRandom.current().nextInt(0, 101);
        double[] row = new double[] { one, two, three };
        testData[i] = row;
    }
    l2svmPredict.setMatrix("X", testData);
    double[][] testLabels = new double[150][1];
    for (int i = 0; i < 150; i++) {
        int one = ThreadLocalRandom.current().nextInt(1, 3);
        double[] row = new double[] { one };
        testLabels[i] = row;
    }
    l2svmPredict.setMatrix("Y", testLabels);
    l2svmPredict.setMatrix("w", model);
    l2svmPredict.setScalar("fmt", "csv");
    ResultVariables l2svmPredictResults = l2svmPredict.executeScript();
    double[][] scores = l2svmPredictResults.getMatrix("scores");
    log.debug("SCORES:");
    log.debug(displayMatrix(scores));
    double[][] confusionMatrix = l2svmPredictResults.getMatrix("confusion_mat");
    log.debug("CONFUSION MATRIX:");
    log.debug(displayMatrix(confusionMatrix));
    conn.close();
}

Example 23 with ResultVariables

use of org.apache.sysml.api.jmlc.ResultVariables in project systemml by apache.

the class FrameIndexingAppendTest method execDMLScriptviaJMLC.

private static ArrayList<String[][]> execDMLScriptviaJMLC(String testname, String[][] F1, String[][] M, boolean modelReuse) throws IOException {
    Timing time = new Timing(true);
    ArrayList<String[][]> ret = new ArrayList<String[][]>();
    // establish connection to SystemML
    Connection conn = new Connection();
    try {
        // prepare input arguments
        HashMap<String, String> args = new HashMap<String, String>();
        args.put("$TRANSFORM_SPEC1", "{ \"ids\": true ,\"recode\": [ 1, 2] }");
        args.put("$TRANSFORM_SPEC2", "{ \"ids\": true ,\"recode\": [ 1] }");
        // read and precompile script
        String script = conn.readScript(SCRIPT_DIR + TEST_DIR + testname + ".dml");
        PreparedScript pstmt = conn.prepareScript(script, args, new String[] { "F1", "M" }, new String[] { "F2" }, false);
        if (modelReuse)
            pstmt.setFrame("M", M, true);
        // execute script multiple times
        for (int i = 0; i < nRuns; i++) {
            // bind input parameters
            if (!modelReuse)
                pstmt.setFrame("M", M);
            pstmt.setFrame("F1", F1);
            // execute script
            ResultVariables rs = pstmt.executeScript();
            // get output parameter
            String[][] Y = rs.getFrame("F2");
            // keep result for comparison
            ret.add(Y);
        }
    } catch (Exception ex) {
        ex.printStackTrace();
        throw new IOException(ex);
    } finally {
        IOUtilFunctions.closeSilently(conn);
    }
    System.out.println("JMLC scoring w/ " + nRuns + " runs in " + time.stop() + "ms.");
    return ret;
}

Example 24 with ResultVariables

use of org.apache.sysml.api.jmlc.ResultVariables in project systemml by apache.

the class FrameTransformTest method execDMLScriptviaJMLC.

private static ArrayList<double[][]> execDMLScriptviaJMLC(String testname, String[][] X, String[][] M, boolean modelReuse) throws IOException {
    Timing time = new Timing(true);
    ArrayList<double[][]> ret = new ArrayList<double[][]>();
    // establish connection to SystemML
    Connection conn = new Connection();
    try {
        // prepare input arguments
        HashMap<String, String> args = new HashMap<String, String>();
        args.put("$TRANSFORM_SPEC", "{ \"ids\": true ,\"recode\": [ 1, 2, 3] }");
        // read and precompile script
        String script = conn.readScript(SCRIPT_DIR + TEST_DIR + testname + ".dml");
        PreparedScript pstmt = conn.prepareScript(script, args, new String[] { "X", "M" }, new String[] { "Y" }, false);
        if (modelReuse)
            pstmt.setFrame("M", M, true);
        // execute script multiple times
        for (int i = 0; i < nRuns; i++) {
            // bind input parameters
            if (!modelReuse)
                pstmt.setFrame("M", M);
            pstmt.setFrame("X", X);
            // execute script
            ResultVariables rs = pstmt.executeScript();
            // get output parameter
            double[][] Y = rs.getMatrix("Y");
            // keep result for comparison
            ret.add(Y);
        }
    } catch (Exception ex) {
        ex.printStackTrace();
        throw new IOException(ex);
    } finally {
        IOUtilFunctions.closeSilently(conn);
    }
    System.out.println("JMLC scoring w/ " + nRuns + " runs in " + time.stop() + "ms.");
    return ret;
}

Example 25 with ResultVariables

use of org.apache.sysml.api.jmlc.ResultVariables in project systemml by apache.

the class MulticlassSVMScoreTest method execDMLScriptviaJMLC.

private static ArrayList<double[][]> execDMLScriptviaJMLC(ArrayList<double[][]> X, boolean flags) throws IOException {
    Timing time = new Timing(true);
    ArrayList<double[][]> ret = new ArrayList<double[][]>();
    // establish connection to SystemML
    Connection conn = !flags ? new Connection() : new Connection(ConfigType.PARALLEL_CP_MATRIX_OPERATIONS, ConfigType.PARALLEL_LOCAL_OR_REMOTE_PARFOR, ConfigType.ALLOW_DYN_RECOMPILATION);
    try {
        // For now, JMLC pipeline only allows dml
        boolean parsePyDML = false;
        // read and precompile script
        String script = conn.readScript(SCRIPT_DIR + TEST_DIR + TEST_NAME + ".dml");
        PreparedScript pstmt = conn.prepareScript(script, new String[] { "X", "W" }, new String[] { "predicted_y" }, parsePyDML);
        // read model
        String modelData = conn.readScript(SCRIPT_DIR + TEST_DIR + MODEL_FILE);
        double[][] W = conn.convertToDoubleMatrix(modelData, rows, cols);
        // execute script multiple times
        for (int i = 0; i < nRuns; i++) {
            // bind input parameters
            pstmt.setMatrix("W", W);
            pstmt.setMatrix("X", X.get(i));
            // execute script
            ResultVariables rs = pstmt.executeScript();
            // get output parameter
            double[][] Y = rs.getMatrix("predicted_y");
            // keep result for comparison
            ret.add(Y);
        }
    } catch (Exception ex) {
        ex.printStackTrace();
        throw new IOException(ex);
    } finally {
        if (conn != null)
            conn.close();
    }
    System.out.println("JMLC scoring w/ " + nRuns + " runs in " + time.stop() + "ms.");
    return ret;
}