Examples with Config edu.neu.ccs.pyramid.configuration.Config used on opensource projects

Example 46 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class App3 method main.

public static void main(String[] args) throws Exception {
    if (args.length != 1) {
        throw new IllegalArgumentException("Please specify a properties file.");
    }
    Config config = new Config(args[0]);
    Logger logger = Logger.getAnonymousLogger();
    String logFile = config.getString("output.log");
    FileHandler fileHandler = null;
    if (!logFile.isEmpty()) {
        new File(logFile).getParentFile().mkdirs();
        //todo should append?
        fileHandler = new FileHandler(logFile, true);
        java.util.logging.Formatter formatter = new SimpleFormatter();
        fileHandler.setFormatter(formatter);
        logger.addHandler(fileHandler);
        logger.setUseParentHandlers(false);
    }
    logger.info(config.toString());
    if (fileHandler != null) {
        fileHandler.close();
    }
    File output = new File(config.getString("output.folder"));
    output.mkdirs();
    Config app1Config = createApp1Config(config);
    Config app2Config = createApp2Config(config);
    App1.main(app1Config);
    App2.main(app2Config);
}

Example 47 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class App6 method main.

public static void main(String[] args) throws Exception {
    if (args.length != 1) {
        throw new IllegalArgumentException("Please specify a properties file.");
    }
    Config config = new Config(args[0]);
    System.out.println(config);
    if (config.getBoolean("train")) {
        train(config);
    }
    if (config.getBoolean("test")) {
        test(config);
    }
}

Example 48 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class App6 method report.

static void report(Config config, MultiLabelClfDataSet dataSet, String dataName) throws Exception {
    System.out.println("generating reports for data set " + dataName);
    String output = config.getString("output.folder");
    String modelName = "model_crf";
    File analysisFolder = new File(new File(output, "reports_crf"), dataName + "_reports");
    analysisFolder.mkdirs();
    FileUtils.cleanDirectory(analysisFolder);
    CMLCRF crf = (CMLCRF) Serialization.deserialize(new File(output, modelName));
    PluginPredictor<CMLCRF> predictorTmp = null;
    String predictTarget = config.getString("predict.target");
    switch(predictTarget) {
        case "subsetAccuracy":
            predictorTmp = new SubsetAccPredictor(crf);
            break;
        case "instanceFMeasure":
            predictorTmp = new InstanceF1Predictor(crf);
            break;
        default:
            throw new IllegalArgumentException("predict.target must be subsetAccuracy or instanceFMeasure");
    }
    // just to make Lambda expressions happy
    final PluginPredictor<CMLCRF> predictor = predictorTmp;
    MLMeasures mlMeasures = new MLMeasures(predictor, dataSet);
    mlMeasures.getMacroAverage().setLabelTranslator(crf.getLabelTranslator());
    System.out.println("performance on dataset " + dataName);
    System.out.println(mlMeasures);
    boolean simpleCSV = true;
    if (simpleCSV) {
        //            System.out.println("start generating simple CSV report");
        double probThreshold = config.getDouble("report.classProbThreshold");
        File csv = new File(analysisFolder, "report.csv");
        List<String> strs = IntStream.range(0, dataSet.getNumDataPoints()).parallel().mapToObj(i -> CRFInspector.simplePredictionAnalysis(crf, predictor, dataSet, i, probThreshold)).collect(Collectors.toList());
        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            String str = strs.get(i);
            sb.append(str);
        }
        FileUtils.writeStringToFile(csv, sb.toString(), false);
    //            System.out.println("finish generating simple CSV report");
    }
    boolean dataInfoToJson = true;
    if (dataInfoToJson) {
        //            System.out.println("start writing data info to json");
        Set<String> modelLabels = IntStream.range(0, crf.getNumClasses()).mapToObj(i -> crf.getLabelTranslator().toExtLabel(i)).collect(Collectors.toSet());
        Set<String> dataSetLabels = DataSetUtil.gatherLabels(dataSet).stream().map(i -> dataSet.getLabelTranslator().toExtLabel(i)).collect(Collectors.toSet());
        JsonGenerator jsonGenerator = new JsonFactory().createGenerator(new File(analysisFolder, "data_info.json"), JsonEncoding.UTF8);
        jsonGenerator.writeStartObject();
        jsonGenerator.writeStringField("dataSet", dataName);
        jsonGenerator.writeNumberField("numClassesInModel", crf.getNumClasses());
        jsonGenerator.writeNumberField("numClassesInDataSet", dataSetLabels.size());
        jsonGenerator.writeNumberField("numClassesInModelDataSetCombined", dataSet.getNumClasses());
        Set<String> modelNotDataLabels = SetUtil.complement(modelLabels, dataSetLabels);
        Set<String> dataNotModelLabels = SetUtil.complement(dataSetLabels, modelLabels);
        jsonGenerator.writeNumberField("numClassesInDataSetButNotModel", dataNotModelLabels.size());
        jsonGenerator.writeNumberField("numClassesInModelButNotDataSet", modelNotDataLabels.size());
        jsonGenerator.writeArrayFieldStart("classesInDataSetButNotModel");
        for (String label : dataNotModelLabels) {
            jsonGenerator.writeObject(label);
        }
        jsonGenerator.writeEndArray();
        jsonGenerator.writeArrayFieldStart("classesInModelButNotDataSet");
        for (String label : modelNotDataLabels) {
            jsonGenerator.writeObject(label);
        }
        jsonGenerator.writeEndArray();
        jsonGenerator.writeNumberField("labelCardinality", dataSet.labelCardinality());
        jsonGenerator.writeEndObject();
        jsonGenerator.close();
    //            System.out.println("finish writing data info to json");
    }
    boolean modelConfigToJson = true;
    if (modelConfigToJson) {
        //            System.out.println("start writing model config to json");
        ObjectMapper objectMapper = new ObjectMapper();
        objectMapper.writeValue(new File(analysisFolder, "model_config.json"), config);
    //            System.out.println("finish writing model config to json");
    }
    boolean performanceToJson = true;
    if (performanceToJson) {
        ObjectMapper objectMapper = new ObjectMapper();
        objectMapper.writeValue(new File(analysisFolder, "performance.json"), mlMeasures);
    }
    boolean individualPerformance = true;
    if (individualPerformance) {
        //            System.out.println("start writing individual label performance to json");
        ObjectMapper objectMapper = new ObjectMapper();
        objectMapper.writeValue(new File(analysisFolder, "individual_performance.json"), mlMeasures.getMacroAverage());
    //            System.out.println("finish writing individual label performance to json");
    }
    System.out.println("reports generated");
}

Example 49 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMEN method reportF1Prediction.

private static void reportF1Prediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test set with the instance F1 optimal predictor");
    String output = config.getString("output.dir");
    PluginF1 pluginF1 = new PluginF1(cbm);
    List<MultiLabel> support = (List<MultiLabel>) Serialization.deserialize(new File(output, "support"));
    pluginF1.setSupport(support);
    pluginF1.setPiThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = pluginF1.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance F1 optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_f1_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println("test performance is saved to " + performanceFile.toString());
    // Here we do not use approximation
    double[] setProbs = IntStream.range(0, predictions.length).parallel().mapToDouble(i -> cbm.predictAssignmentProb(dataSet.getRow(i), predictions[i])).toArray();
    File predictionFile = Paths.get(output, "test_predictions", "instance_f1_optimal", "predictions.txt").toFile();
    try (BufferedWriter br = new BufferedWriter(new FileWriter(predictionFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(predictions[i].toString());
            br.write(":");
            br.write("" + setProbs[i]);
            br.newLine();
        }
    }
    System.out.println("predicted sets and their probabilities are saved to " + predictionFile.getAbsolutePath());
    System.out.println("============================================================");
}

Example 50 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMLR method reportF1Prediction.

private static void reportF1Prediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test set with the instance F1 optimal predictor");
    String output = config.getString("output.dir");
    PluginF1 pluginF1 = new PluginF1(cbm);
    List<MultiLabel> support = (List<MultiLabel>) Serialization.deserialize(new File(output, "support"));
    pluginF1.setSupport(support);
    pluginF1.setPiThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = pluginF1.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance F1 optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_f1_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println("test performance is saved to " + performanceFile.toString());
    // Here we do not use approximation
    double[] setProbs = IntStream.range(0, predictions.length).parallel().mapToDouble(i -> cbm.predictAssignmentProb(dataSet.getRow(i), predictions[i])).toArray();
    File predictionFile = Paths.get(output, "test_predictions", "instance_f1_optimal", "predictions.txt").toFile();
    try (BufferedWriter br = new BufferedWriter(new FileWriter(predictionFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(predictions[i].toString());
            br.write(":");
            br.write("" + setProbs[i]);
            br.newLine();
        }
    }
    System.out.println("predicted sets and their probabilities are saved to " + predictionFile.getAbsolutePath());
    System.out.println("============================================================");
}