Example usage for org.apache.mahout.classifier RegressionResultAnalyzer toString

Introduction

In this page you can find the example usage for org.apache.mahout.classifier RegressionResultAnalyzer toString.

Prototype

@Override
    public String toString() 

Source Link

Usage

From source file:guipart.view.GUIOverviewController.java

@FXML
void handleClassifyRF(ActionEvent event) throws IOException {

    String outputFile = "data/out";

    Path dataPath = new Path(textFieldCSVRF.getText()); // test data path
    Path datasetPath = new Path(textFieldDatasetRF.getText()); //info file about data set
    Path modelPath = new Path(textFieldModelRF.getText()); // path where the forest is stored
    Path outputPath = new Path(outputFile); // path to predictions file, if null do not output the predictions

    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);

    FileSystem outFS = FileSystem.get(conf);

    System.out.println("Loading the forest");
    DecisionForest forest = DecisionForest.load(conf, modelPath);

    if (forest == null)
        System.err.println("No decision forest found!");

    // load the dataset
    Dataset dataset = Dataset.load(conf, datasetPath);
    DataConverter converter = new DataConverter(dataset);

    System.out.println("Sequential classification");
    long time = System.currentTimeMillis();

    Random rng = RandomUtils.getRandom();

    List<double[]> resList = Lists.newArrayList();
    if (fs.getFileStatus(dataPath).isDir()) {
        //the input is a directory of files
        Utils.rfTestDirectory(outputPath, converter, forest, dataset, resList, rng, fs, dataPath, outFS,
                guiPart);//  ww  w .  j a va 2  s  . co  m
    } else {
        // the input is one single file
        Utils.rfTestFile(dataPath, outputPath, converter, forest, dataset, resList, rng, outFS, fs, guiPart);
    }

    time = System.currentTimeMillis() - time;
    //log.info("Classification Time: {}", DFUtils.elapsedTime(time));
    System.out.println("Classification time: " + DFUtils.elapsedTime(time));

    if (dataset.isNumerical(dataset.getLabelId())) {

        RegressionResultAnalyzer regressionAnalyzer = new RegressionResultAnalyzer();
        double[][] results = new double[resList.size()][2];
        regressionAnalyzer.setInstances(resList.toArray(results));
        //log.info("{}", regressionAnalyzer);
        System.out.println(regressionAnalyzer.toString());

    } else {
        ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
        for (double[] r : resList) {
            analyzer.addInstance(dataset.getLabelString(r[0]),
                    new ClassifierResult(dataset.getLabelString(r[1]), 1.0));
        }
        //log.info("{}", analyzer);
        System.out.println(analyzer.toString());
        textAnalyze.setText(analyzer.toString());
    }

}

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From source file:javaapplication3.RunRandomForestSeq.java

public static void main(String[] args) throws IOException {

    String outputFile = "data/out";
    String inputFile = "data/DataFraud1MTest.csv";
    String modelFile = "data/forest.seq";
    String infoFile = "data/DataFraud1M.info";

    Path dataPath = new Path(inputFile); // test data path
    Path datasetPath = new Path(infoFile);
    Path modelPath = new Path(modelFile); // path where the forest is stored
    Path outputPath = new Path(outputFile); // path to predictions file, if null do not output the predictions

    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);

    FileSystem outFS = FileSystem.get(conf);

    //log.info("Loading the forest...");
    System.out.println("Loading the forest");
    DecisionForest forest = DecisionForest.load(conf, modelPath);

    if (forest == null)
        System.err.println("No decision forest found!");
    //log.error("No Decision Forest found!");

    // load the dataset
    Dataset dataset = Dataset.load(conf, datasetPath);
    DataConverter converter = new DataConverter(dataset);

    //log.info("Sequential classification...");
    System.out.println("Sequential classification");
    long time = System.currentTimeMillis();

    Random rng = RandomUtils.getRandom();

    List<double[]> resList = Lists.newArrayList();
    if (fs.getFileStatus(dataPath).isDir()) {
        //the input is a directory of files
        testDirectory(outputPath, converter, forest, dataset, resList, rng, fs, dataPath, outFS);
    } else {/*from   w ww .j a v a  2s .  c  om*/
        // the input is one single file
        testFile(dataPath, outputPath, converter, forest, dataset, resList, rng, outFS, fs);
    }

    time = System.currentTimeMillis() - time;
    //log.info("Classification Time: {}", DFUtils.elapsedTime(time));
    System.out.println("Classification time: " + DFUtils.elapsedTime(time));

    if (dataset.isNumerical(dataset.getLabelId())) {

        RegressionResultAnalyzer regressionAnalyzer = new RegressionResultAnalyzer();
        double[][] results = new double[resList.size()][2];
        regressionAnalyzer.setInstances(resList.toArray(results));
        //log.info("{}", regressionAnalyzer);
        System.out.println(regressionAnalyzer.toString());

    } else {
        ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
        for (double[] r : resList) {
            analyzer.addInstance(dataset.getLabelString(r[0]),
                    new ClassifierResult(dataset.getLabelString(r[1]), 1.0));
        }
        //log.info("{}", analyzer);
        System.out.println(analyzer.toString());
    }

}

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