Examples with ScoreModel edu.cmu.tetradapp.ui.model.ScoreModel used on opensource projects

Example 1 with ScoreModel

use of edu.cmu.tetradapp.ui.model.ScoreModel in project tetrad by cmu-phil.

the class GeneralAlgorithmEditor method initComponents.

private void initComponents() {
    algoDescTextArea.setWrapStyleWord(true);
    algoDescTextArea.setLineWrap(true);
    algoDescTextArea.setEditable(false);
    populateAlgoTypeOptions(algoTypeOpts);
    knowledgeChkBox.addActionListener((e) -> {
        refreshAlgorithmList();
    });
    linearVarChkBox.addActionListener((ActionEvent e) -> {
        refreshTestAndScoreList();
    });
    gaussianVarChkBox.addActionListener((ActionEvent e) -> {
        refreshTestAndScoreList();
    });
    algorithmList.addListSelectionListener((e) -> {
        if (!(e.getValueIsAdjusting() || algorithmList.isSelectionEmpty())) {
            setAlgorithmDescription();
            refreshTestAndScoreList();
            validateAlgorithmOption();
        }
    });
    paramSetFwdBtn.addActionListener((e) -> {
        AlgorithmModel algoModel = algorithmList.getSelectedValue();
        IndependenceTestModel indTestModel = indTestComboBox.getItemAt(indTestComboBox.getSelectedIndex());
        ScoreModel scoreModel = scoreComboBox.getItemAt(scoreComboBox.getSelectedIndex());
        if (isValid(algoModel, indTestModel, scoreModel)) {
            setParameterPanel(algoModel, indTestModel, scoreModel);
            changeCard(PARAMETER_CARD);
        }
    });
    indTestComboBox.addActionListener((e) -> {
        if (!updatingTestModels && indTestComboBox.getSelectedIndex() >= 0) {
            AlgorithmModel algoModel = algorithmList.getSelectedValue();
            Map<DataType, IndependenceTestModel> map = defaultIndTestModels.get(algoModel);
            if (map == null) {
                map = new EnumMap<>(DataType.class);
                defaultIndTestModels.put(algoModel, map);
            }
            map.put(dataType, indTestComboBox.getItemAt(indTestComboBox.getSelectedIndex()));
        }
    });
    scoreComboBox.addActionListener((e) -> {
        if (!updatingScoreModels && scoreComboBox.getSelectedIndex() >= 0) {
            AlgorithmModel algoModel = algorithmList.getSelectedValue();
            Map<DataType, ScoreModel> map = defaultScoreModels.get(algoModel);
            if (map == null) {
                map = new EnumMap<>(DataType.class);
                defaultScoreModels.put(algoModel, map);
            }
            map.put(dataType, scoreComboBox.getItemAt(scoreComboBox.getSelectedIndex()));
        }
    });
    mainPanel.add(new AlgorithmCard(), ALGORITHM_CARD);
    mainPanel.add(new ParameterCard(), PARAMETER_CARD);
    mainPanel.add(new GraphCard(), GRAPH_CARD);
    mainPanel.setPreferredSize(new Dimension(940, 640));
    setLayout(new BorderLayout());
    add(mainPanel, BorderLayout.CENTER);
// add(new JScrollPane(mainPanel), BorderLayout.CENTER);
}

Example 2 with ScoreModel

use of edu.cmu.tetradapp.ui.model.ScoreModel in project tetrad by cmu-phil.

the class GeneralAlgorithmEditor method restorePreviousState.

private void restorePreviousState(Map<String, Object> models) {
    Object obj = models.get(LINEAR_PARAM);
    if ((obj != null) && (obj instanceof Boolean)) {
        linearVarChkBox.setSelected((Boolean) obj);
    }
    obj = models.get(GAUSSIAN_PARAM);
    if ((obj != null) && (obj instanceof Boolean)) {
        gaussianVarChkBox.setSelected((Boolean) obj);
    }
    obj = models.get(KNOWLEDGE_PARAM);
    if ((obj != null) && (obj instanceof Boolean)) {
        knowledgeChkBox.setSelected((Boolean) obj);
    }
    obj = models.get(ALGO_TYPE_PARAM);
    if ((obj != null) && (obj instanceof String)) {
        String actCmd = String.valueOf(obj);
        Optional<JRadioButton> opt = algoTypeOpts.stream().filter(e -> e.getActionCommand().equals(actCmd)).findFirst();
        if (opt.isPresent()) {
            opt.get().setSelected(true);
        }
    }
    refreshAlgorithmList();
    refreshTestAndScoreList();
    obj = models.get(ALGO_PARAM);
    if ((obj != null) && (obj instanceof AlgorithmModel)) {
        String value = ((AlgorithmModel) obj).toString();
        Enumeration<AlgorithmModel> enums = algoModels.elements();
        while (enums.hasMoreElements()) {
            AlgorithmModel model = enums.nextElement();
            if (model.toString().equals(value)) {
                models.put(ALGO_PARAM, model);
                algorithmList.setSelectedValue(model, true);
                String title = String.format("Algorithm: %s", model.getAlgorithm().getAnnotation().name());
                algorithmGraphTitle.setText(title);
                break;
            }
        }
    }
    obj = models.get(IND_TEST_PARAM);
    if ((obj != null) && (obj instanceof IndependenceTestModel)) {
        String value = ((IndependenceTestModel) obj).toString();
        ComboBoxModel<IndependenceTestModel> comboBoxModels = indTestComboBox.getModel();
        int size = comboBoxModels.getSize();
        for (int i = 0; i < size; i++) {
            IndependenceTestModel model = comboBoxModels.getElementAt(i);
            if (model.toString().equals(value)) {
                models.put(IND_TEST_PARAM, model);
                indTestComboBox.getModel().setSelectedItem(model);
                break;
            }
        }
    }
    obj = models.get(SCORE_PARAM);
    if ((obj != null) && (obj instanceof ScoreModel)) {
        String value = ((ScoreModel) obj).toString();
        ComboBoxModel<ScoreModel> comboBoxModels = scoreComboBox.getModel();
        int size = comboBoxModels.getSize();
        for (int i = 0; i < size; i++) {
            ScoreModel model = comboBoxModels.getElementAt(i);
            if (model.toString().equals(value)) {
                models.put(SCORE_PARAM, model);
                scoreComboBox.getModel().setSelectedItem(model);
                break;
            }
        }
    }
}

Example 3 with ScoreModel

use of edu.cmu.tetradapp.ui.model.ScoreModel in project tetrad by cmu-phil.

the class GeneralAlgorithmEditor method refreshScoreList.

private void refreshScoreList() {
    updatingScoreModels = true;
    scoreComboBox.removeAllItems();
    AlgorithmModel algoModel = algorithmList.getSelectedValue();
    if (algoModel != null && algoModel.isRequiredScore()) {
        boolean linear = linearVarChkBox.isSelected();
        boolean gaussian = gaussianVarChkBox.isSelected();
        List<ScoreModel> models = ScoreModels.getInstance().getModels(dataType);
        List<ScoreModel> scoreModels = new LinkedList<>();
        if (linear && gaussian) {
            models.stream().filter(e -> e.getScore().getClazz().isAnnotationPresent(Linear.class)).filter(e -> e.getScore().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> scoreModels.add(e));
        } else if (linear) {
            models.stream().filter(e -> e.getScore().getClazz().isAnnotationPresent(Linear.class)).filter(e -> !e.getScore().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> scoreModels.add(e));
        } else if (gaussian) {
            models.stream().filter(e -> !e.getScore().getClazz().isAnnotationPresent(Linear.class)).filter(e -> e.getScore().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> scoreModels.add(e));
        } else {
            models.stream().forEach(e -> scoreModels.add(e));
        }
        // or BDeu score for discrete data
        if (TsImages.class.equals(algoModel.getAlgorithm().getClazz())) {
            switch(dataType) {
                case Continuous:
                    scoreModels.stream().filter(e -> e.getScore().getClazz().equals(SemBicScore.class)).forEach(e -> scoreComboBox.addItem(e));
                    break;
                case Discrete:
                    scoreModels.stream().filter(e -> e.getScore().getClazz().equals(BdeuScore.class)).forEach(e -> scoreComboBox.addItem(e));
                    break;
            }
        } else {
            scoreModels.forEach(e -> scoreComboBox.addItem(e));
        }
    }
    updatingScoreModels = false;
    if (scoreComboBox.getItemCount() > 0) {
        scoreComboBox.setEnabled(true);
        Map<DataType, ScoreModel> map = defaultScoreModels.get(algoModel);
        if (map == null) {
            map = new EnumMap<>(DataType.class);
            defaultScoreModels.put(algoModel, map);
        }
        ScoreModel scoreModel = map.get(dataType);
        if (scoreModel == null) {
            scoreModel = ScoreModels.getInstance().getDefaultModel(dataType);
            if (scoreModel == null) {
                scoreModel = scoreComboBox.getItemAt(0);
            }
        }
        scoreComboBox.setSelectedItem(scoreModel);
    } else {
        scoreComboBox.setEnabled(false);
    }
}

Example 4 with ScoreModel

use of edu.cmu.tetradapp.ui.model.ScoreModel in project tetrad by cmu-phil.

the class GeneralAlgorithmEditor method doRemoteCompute.

private void doRemoteCompute(final GeneralAlgorithmRunner runner, final HpcAccount hpcAccount) throws Exception {
    // **********************
    // Show progress panel *
    // **********************
    Frame ancestor = (Frame) JOptionUtils.centeringComp().getTopLevelAncestor();
    final JDialog progressDialog = new JDialog(ancestor, "HPC Job Submission's Progress...", false);
    Dimension progressDim = new Dimension(500, 150);
    JTextArea progressTextArea = new JTextArea();
    progressTextArea.setPreferredSize(progressDim);
    progressTextArea.setEditable(false);
    JScrollPane progressScroller = new JScrollPane(progressTextArea);
    progressScroller.setAlignmentX(LEFT_ALIGNMENT);
    progressDialog.setLayout(new BorderLayout());
    progressDialog.getContentPane().add(progressScroller, BorderLayout.CENTER);
    progressDialog.pack();
    Dimension screenDim = Toolkit.getDefaultToolkit().getScreenSize();
    progressDialog.setLocation((screenDim.width - progressDim.width) / 2, (screenDim.height - progressDim.height) / 2);
    progressDialog.setVisible(true);
    int totalProcesses = 4;
    String newline = "\n";
    String tab = "\t";
    int progressTextLength = 0;
    DataModel dataModel = runner.getDataModel();
    // 1. Generate temp file
    Path file = null;
    Path prior = null;
    try {
        // ****************************
        // Data Preparation Progress *
        // ****************************
        String dataMessage = String.format("1/%1$d Data Preparation", totalProcesses);
        progressTextArea.append(dataMessage);
        progressTextArea.append(tab);
        progressTextLength = progressTextArea.getText().length();
        progressTextArea.append("Preparing...");
        progressTextArea.updateUI();
        file = Files.createTempFile("Tetrad-data-", ".txt");
        // LOGGER.info(file.toAbsolutePath().toString());
        List<String> tempLine = new ArrayList<>();
        // Header
        List<Node> variables = dataModel.getVariables();
        if ((variables == null || variables.isEmpty()) && runner.getSourceGraph() != null) {
            variables = runner.getSourceGraph().getNodes();
        }
        String vars = StringUtils.join(variables.toArray(), tab);
        tempLine.add(vars);
        // Data
        DataSet dataSet = (DataSet) dataModel;
        for (int i = 0; i < dataSet.getNumRows(); i++) {
            String line = null;
            for (int j = 0; j < dataSet.getNumColumns(); j++) {
                String cell = null;
                if (dataSet.isContinuous()) {
                    cell = String.valueOf(dataSet.getDouble(i, j));
                } else {
                    cell = String.valueOf(dataSet.getInt(i, j));
                }
                if (line == null) {
                    line = cell;
                } else {
                    line = line + "\t" + cell;
                }
            }
            tempLine.add(line);
        }
        // for (String line : tempLine) {
        // LOGGER.info(line);
        // }
        Files.write(file, tempLine);
        // Get file's MD5 hash and use it as its identifier
        String datasetMd5 = MessageDigestHash.computeMD5Hash(file);
        progressTextArea.replaceRange("Done", progressTextLength, progressTextArea.getText().length());
        progressTextArea.append(newline);
        progressTextArea.updateUI();
        // ***************************************
        // Prior Knowledge Preparation Progress *
        // ***************************************
        String priorMessage = String.format("2/%1$d Prior Knowledge Preparation", totalProcesses);
        progressTextArea.append(priorMessage);
        progressTextArea.append(tab);
        progressTextLength = progressTextArea.getText().length();
        progressTextArea.append("Preparing...");
        progressTextArea.updateUI();
        // 2. Generate temp prior knowledge file
        Knowledge2 knowledge = (Knowledge2) dataModel.getKnowledge();
        if (knowledge != null && !knowledge.isEmpty()) {
            prior = Files.createTempFile(file.getFileName().toString(), ".prior");
            knowledge.saveKnowledge(Files.newBufferedWriter(prior));
            progressTextArea.replaceRange("Done", progressTextLength, progressTextArea.getText().length());
            progressTextArea.append(newline);
            progressTextArea.updateUI();
        } else {
            progressTextArea.replaceRange("Skipped", progressTextLength, progressTextArea.getText().length());
            progressTextArea.append(newline);
            progressTextArea.updateUI();
        }
        // Get knowledge file's MD5 hash and use it as its identifier
        String priorKnowledgeMd5 = null;
        if (prior != null) {
            priorKnowledgeMd5 = MessageDigestHash.computeMD5Hash(prior);
        }
        // *******************************************
        // Algorithm Parameter Preparation Progress *
        // *******************************************
        String algorMessage = String.format("3/%1$d Algorithm Preparation", totalProcesses);
        progressTextArea.append(algorMessage);
        progressTextArea.append(tab);
        progressTextLength = progressTextArea.getText().length();
        progressTextArea.append("Preparing...");
        progressTextArea.updateUI();
        // 3.1 Algorithm Id, Independent Test Id, Score Id
        AlgorithmModel algoModel = algorithmList.getSelectedValue();
        String algoId = algoModel.getAlgorithm().getAnnotation().command();
        // Test
        String testId = null;
        if (indTestComboBox.isEnabled()) {
            IndependenceTestModel indTestModel = indTestComboBox.getItemAt(indTestComboBox.getSelectedIndex());
            testId = indTestModel.getIndependenceTest().getAnnotation().command();
        }
        // Score
        String scoreId = null;
        if (scoreComboBox.isEnabled()) {
            ScoreModel scoreModel = scoreComboBox.getItemAt(scoreComboBox.getSelectedIndex());
            scoreId = scoreModel.getScore().getAnnotation().command();
        }
        // 3.2 Parameters
        AlgorithmParamRequest algorithmParamRequest = new AlgorithmParamRequest();
        // Test and score
        algorithmParamRequest.setTestId(testId);
        algorithmParamRequest.setScoreId(scoreId);
        // Dataset and Prior paths
        String datasetPath = file.toAbsolutePath().toString();
        LOGGER.info(datasetPath);
        algorithmParamRequest.setDatasetPath(datasetPath);
        algorithmParamRequest.setDatasetMd5(datasetMd5);
        if (prior != null) {
            String priorKnowledgePath = prior.toAbsolutePath().toString();
            LOGGER.info(priorKnowledgePath);
            algorithmParamRequest.setPriorKnowledgePath(priorKnowledgePath);
            algorithmParamRequest.setPriorKnowledgeMd5(priorKnowledgeMd5);
        }
        // VariableType
        if (dataModel.isContinuous()) {
            algorithmParamRequest.setVariableType("continuous");
        } else if (dataModel.isDiscrete()) {
            algorithmParamRequest.setVariableType("discrete");
        } else {
            algorithmParamRequest.setVariableType("mixed");
        }
        // FileDelimiter
        // Pre-determined
        String fileDelimiter = "tab";
        algorithmParamRequest.setFileDelimiter(fileDelimiter);
        Set<AlgorithmParameter> AlgorithmParameters = new HashSet<>();
        Parameters parameters = runner.getParameters();
        List<String> parameterNames = runner.getAlgorithm().getParameters();
        for (String parameter : parameterNames) {
            String value = parameters.get(parameter).toString();
            LOGGER.info("parameter: " + parameter + "\tvalue: " + value);
            if (value != null) {
                AlgorithmParameter algorParam = new AlgorithmParameter();
                algorParam.setParameter(parameter);
                algorParam.setValue(value);
                AlgorithmParameters.add(algorParam);
            }
        }
        algorithmParamRequest.setAlgorithmParameters(AlgorithmParameters);
        String maxHeapSize = null;
        do {
            maxHeapSize = JOptionPane.showInputDialog(progressDialog, "Enter Your Request Java Max Heap Size (GB):", "5");
        } while (maxHeapSize != null && !StringUtils.isNumeric(maxHeapSize));
        if (maxHeapSize != null) {
            JvmOptions jvmOptions = new JvmOptions();
            jvmOptions.setMaxHeapSize(Integer.parseInt(maxHeapSize));
            algorithmParamRequest.setJvmOptions(jvmOptions);
        }
        // Hpc parameters
        final HpcAccountManager hpcAccountManager = desktop.getHpcAccountManager();
        JsonWebToken jsonWebToken = HpcAccountUtils.getJsonWebToken(hpcAccountManager, hpcAccount);
        if (jsonWebToken.getWallTime() != null) {
            // User allowed to customize the job's wall time
            String[] wallTime = jsonWebToken.getWallTime();
            Object userwallTime = JOptionPane.showInputDialog(progressDialog, "Wall Time:", "Choose Your Wall Time (in Hour)", JOptionPane.QUESTION_MESSAGE, null, wallTime, wallTime[0]);
            if (wallTime != null && userwallTime != null) {
                HpcParameter hpcParameter = new HpcParameter();
                hpcParameter.setKey("walltime");
                hpcParameter.setValue(userwallTime.toString());
                LOGGER.info("walltime: " + userwallTime.toString());
                Set<HpcParameter> hpcParameters = new HashSet<>();
                hpcParameters.add(hpcParameter);
                algorithmParamRequest.setHpcParameters(hpcParameters);
            }
        }
        progressTextArea.replaceRange("Done", progressTextLength, progressTextArea.getText().length());
        progressTextArea.append(newline);
        progressTextArea.updateUI();
        // ********************************
        // Adding HPC Job Queue Progress *
        // ********************************
        String dbMessage = String.format("4/%1$d HPC Job Queue Submission", totalProcesses);
        progressTextArea.append(dbMessage);
        progressTextArea.append(tab);
        progressTextLength = progressTextArea.getText().length();
        progressTextArea.append("Preparing...");
        progressTextArea.updateUI();
        HpcJobManager hpcJobManager = desktop.getHpcJobManager();
        // 4.1 Save HpcJobInfo
        hpcJobInfo = new HpcJobInfo();
        hpcJobInfo.setAlgoId(algoId);
        hpcJobInfo.setAlgorithmParamRequest(algorithmParamRequest);
        hpcJobInfo.setStatus(-1);
        hpcJobInfo.setHpcAccount(hpcAccount);
        hpcJobManager.submitNewHpcJobToQueue(hpcJobInfo, this);
        progressTextArea.replaceRange("Done", progressTextLength, progressTextArea.getText().length());
        progressTextArea.append(newline);
        progressTextArea.updateUI();
        this.jsonResult = null;
        JOptionPane.showMessageDialog(ancestor, "The " + hpcJobInfo.getAlgoId() + " job on the " + hpcJobInfo.getHpcAccount().getConnectionName() + " node is in the queue successfully!");
    } catch (IOException exception) {
        LOGGER.error("", exception);
    } finally {
        progressDialog.setVisible(false);
        progressDialog.dispose();
    }
    (new HpcJobActivityAction("")).actionPerformed(null);
}