Examples with DataType edu.cmu.tetrad.data.DataType used on opensource projects

Example 1 with DataType

use of edu.cmu.tetrad.data.DataType 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 DataType

use of edu.cmu.tetrad.data.DataType 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 3 with DataType

use of edu.cmu.tetrad.data.DataType in project tetrad by cmu-phil.

the class IndependenceTestModels method initDefaultModelMap.

private void initDefaultModelMap() {
    DataType[] dataTypes = DataType.values();
    for (DataType dataType : dataTypes) {
        List<IndependenceTestModel> list = getModels(dataType);
        if (!list.isEmpty()) {
            String property = getProperty(dataType);
            if (property == null) {
                defaultModelMap.put(dataType, list.get(0));
            } else {
                String value = TetradProperties.getInstance().getValue(property);
                if (value == null) {
                    defaultModelMap.put(dataType, list.get(0));
                } else {
                    Optional<IndependenceTestModel> result = list.stream().filter(e -> e.getIndependenceTest().getClazz().getName().equals(value)).findFirst();
                    defaultModelMap.put(dataType, result.isPresent() ? result.get() : list.get(0));
                }
            }
        }
    }
}

Example 4 with DataType

use of edu.cmu.tetrad.data.DataType in project tetrad by cmu-phil.

the class GeneralAlgorithmRunner method execute.

// ============================PUBLIC METHODS==========================//
@Override
public void execute() {
    List<Graph> graphList = new ArrayList<>();
    int i = 0;
    if (getDataModelList().isEmpty()) {
        if (getSourceGraph() != null) {
            Algorithm algo = getAlgorithm();
            if (algo instanceof HasKnowledge) {
                ((HasKnowledge) algo).setKnowledge(getKnowledge());
            }
            graphList.add(algo.search(null, parameters));
        } else {
            throw new IllegalArgumentException("The parent boxes did not include any datasets or graphs. Try opening\n" + "the editors for those boxes and loading or simulating them.");
        }
    } else {
        if (getAlgorithm() instanceof MultiDataSetAlgorithm) {
            for (int k = 0; k < parameters.getInt("numRuns"); k++) {
                List<DataSet> dataSets = getDataModelList().stream().map(e -> (DataSet) e).collect(Collectors.toCollection(ArrayList::new));
                if (dataSets.size() < parameters.getInt("randomSelectionSize")) {
                    throw new IllegalArgumentException("Sorry, the 'random selection size' is greater than " + "the number of data sets.");
                }
                Collections.shuffle(dataSets);
                List<DataModel> sub = new ArrayList<>();
                for (int j = 0; j < parameters.getInt("randomSelectionSize"); j++) {
                    sub.add(dataSets.get(j));
                }
                Algorithm algo = getAlgorithm();
                if (algo instanceof HasKnowledge) {
                    ((HasKnowledge) algo).setKnowledge(getKnowledge());
                }
                graphList.add(((MultiDataSetAlgorithm) algo).search(sub, parameters));
            }
        } else if (getAlgorithm() instanceof ClusterAlgorithm) {
            for (int k = 0; k < parameters.getInt("numRuns"); k++) {
                getDataModelList().forEach(dataModel -> {
                    if (dataModel instanceof ICovarianceMatrix) {
                        ICovarianceMatrix dataSet = (ICovarianceMatrix) dataModel;
                        graphList.add(algorithm.search(dataSet, parameters));
                    } else if (dataModel instanceof DataSet) {
                        DataSet dataSet = (DataSet) dataModel;
                        if (!dataSet.isContinuous()) {
                            throw new IllegalArgumentException("Sorry, you need a continuous dataset for a cluster algorithm.");
                        }
                        graphList.add(algorithm.search(dataSet, parameters));
                    }
                });
            }
        } else {
            getDataModelList().forEach(data -> {
                IKnowledge knowledgeFromData = data.getKnowledge();
                if (!(knowledgeFromData == null || knowledgeFromData.getVariables().isEmpty())) {
                    this.knowledge = knowledgeFromData;
                }
                Algorithm algo = getAlgorithm();
                if (algo instanceof HasKnowledge) {
                    ((HasKnowledge) algo).setKnowledge(getKnowledge());
                }
                DataType algDataType = algo.getDataType();
                if (data.isContinuous() && (algDataType == DataType.Continuous || algDataType == DataType.Mixed)) {
                    graphList.add(algo.search(data, parameters));
                } else if (data.isDiscrete() && (algDataType == DataType.Discrete || algDataType == DataType.Mixed)) {
                    graphList.add(algo.search(data, parameters));
                } else if (data.isMixed() && algDataType == DataType.Mixed) {
                    graphList.add(algo.search(data, parameters));
                } else {
                    throw new IllegalArgumentException("The type of data changed; try opening up the search editor and " + "running the algorithm there.");
                }
            });
        }
    }
    if (getKnowledge().getVariablesNotInTiers().size() < getKnowledge().getVariables().size()) {
        for (Graph graph : graphList) {
            SearchGraphUtils.arrangeByKnowledgeTiers(graph, getKnowledge());
        }
    } else {
        for (Graph graph : graphList) {
            GraphUtils.circleLayout(graph, 225, 200, 150);
        }
    }
    this.graphList = graphList;
}

Example 5 with DataType

use of edu.cmu.tetrad.data.DataType in project tetrad by cmu-phil.

the class TimeoutComparison method compareFromSimulations.

/**
 * Compares algorithms.
 *
 * @param resultsPath Path to the file where the output should be printed.
 * @param simulations The list of simulationWrapper that is used to generate
 * graphs and data for the comparison.
 * @param algorithms The list of algorithms to be compared.
 * @param statistics The list of statistics on which to compare the
 * algorithm, and their utility weights.
 */
public void compareFromSimulations(String resultsPath, Simulations simulations, String outputFileName, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit) {
    this.resultsPath = resultsPath;
    // Create output file.
    try {
        File dir = new File(resultsPath);
        dir.mkdirs();
        File file = new File(dir, outputFileName);
        this.out = new PrintStream(new FileOutputStream(file));
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
    out.println(new Date());
    // Set up simulations--create data and graphs, read in parameters. The parameters
    // are set in the parameters object.
    List<SimulationWrapper> simulationWrappers = new ArrayList<>();
    int numRuns = parameters.getInt("numRuns");
    for (Simulation simulation : simulations.getSimulations()) {
        List<SimulationWrapper> wrappers = getSimulationWrappers(simulation, parameters);
        for (SimulationWrapper wrapper : wrappers) {
            wrapper.createData(wrapper.getSimulationSpecificParameters());
            simulationWrappers.add(wrapper);
        }
    }
    // Set up the algorithms.
    List<AlgorithmWrapper> algorithmWrappers = new ArrayList<>();
    for (Algorithm algorithm : algorithms.getAlgorithms()) {
        List<Integer> _dims = new ArrayList<>();
        List<String> varyingParameters = new ArrayList<>();
        final List<String> parameters1 = algorithm.getParameters();
        for (String name : parameters1) {
            if (parameters.getNumValues(name) > 1) {
                _dims.add(parameters.getNumValues(name));
                varyingParameters.add(name);
            }
        }
        if (varyingParameters.isEmpty()) {
            algorithmWrappers.add(new AlgorithmWrapper(algorithm, parameters));
        } else {
            int[] dims = new int[_dims.size()];
            for (int i = 0; i < _dims.size(); i++) {
                dims[i] = _dims.get(i);
            }
            CombinationGenerator gen = new CombinationGenerator(dims);
            int[] choice;
            while ((choice = gen.next()) != null) {
                AlgorithmWrapper wrapper = new AlgorithmWrapper(algorithm, parameters);
                for (int h = 0; h < dims.length; h++) {
                    String parameter = varyingParameters.get(h);
                    Object[] values = parameters.getValues(parameter);
                    Object value = values[choice[h]];
                    wrapper.setValue(parameter, value);
                }
                algorithmWrappers.add(wrapper);
            }
        }
    }
    // Create the algorithm-simulation wrappers for every combination of algorithm and
    // simulation.
    List<AlgorithmSimulationWrapper> algorithmSimulationWrappers = new ArrayList<>();
    for (SimulationWrapper simulationWrapper : simulationWrappers) {
        for (AlgorithmWrapper algorithmWrapper : algorithmWrappers) {
            DataType algDataType = algorithmWrapper.getDataType();
            DataType simDataType = simulationWrapper.getDataType();
            if (!(algDataType == DataType.Mixed || (algDataType == simDataType))) {
                System.out.println("Type mismatch: " + algorithmWrapper.getDescription() + " / " + simulationWrapper.getDescription());
            }
            if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
                ExternalAlgorithm external = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
                // external.setSimulation(simulationWrapper.getSimulation());
                // external.setPath(dirs.get(simulationWrappers.indexOf(simulationWrapper)));
                // external.setPath(resultsPath);
                external.setSimIndex(simulationWrappers.indexOf(external.getSimulation()));
            }
            algorithmSimulationWrappers.add(new AlgorithmSimulationWrapper(algorithmWrapper, simulationWrapper));
        }
    }
    // Run all of the algorithms and compile statistics.
    double[][][][] allStats = calcStats(algorithmSimulationWrappers, algorithmWrappers, simulationWrappers, statistics, numRuns, timeout, unit);
    // Print out the preliminary information for statistics types, etc.
    if (allStats != null) {
        out.println();
        out.println("Statistics:");
        out.println();
        for (Statistic stat : statistics.getStatistics()) {
            out.println(stat.getAbbreviation() + " = " + stat.getDescription());
        }
    }
    out.println();
    // out.println();
    if (allStats != null) {
        int numTables = allStats.length;
        int numStats = allStats[0][0].length - 1;
        double[][][] statTables = calcStatTables(allStats, Mode.Average, numTables, algorithmSimulationWrappers, numStats, statistics);
        double[] utilities = calcUtilities(statistics, algorithmSimulationWrappers, statTables[0]);
        // Add utilities to table as the last column.
        for (int u = 0; u < numTables; u++) {
            for (int t = 0; t < algorithmSimulationWrappers.size(); t++) {
                statTables[u][t][numStats] = utilities[t];
            }
        }
        int[] newOrder;
        if (isSortByUtility()) {
            newOrder = sort(algorithmSimulationWrappers, utilities);
        } else {
            newOrder = new int[algorithmSimulationWrappers.size()];
            for (int q = 0; q < algorithmSimulationWrappers.size(); q++) {
                newOrder[q] = q;
            }
        }
        out.println("Simulations:");
        out.println();
        // if (simulationWrappers.size() == 1) {
        // out.println(simulationWrappers.get(0).getDescription());
        // } else {
        int i = 0;
        for (SimulationWrapper simulation : simulationWrappers) {
            out.print("Simulation " + (++i) + ": ");
            out.println(simulation.getDescription());
            out.println();
            printParameters(simulation.getParameters(), simulation.getSimulationSpecificParameters(), out);
            // for (String param : simulation.getParameters()) {
            // out.println(param + " = " + simulation.getValue(param));
            // }
            out.println();
        }
        // }
        out.println("Algorithms:");
        out.println();
        for (int t = 0; t < algorithmSimulationWrappers.size(); t++) {
            AlgorithmSimulationWrapper wrapper = algorithmSimulationWrappers.get(t);
            if (wrapper.getSimulationWrapper() == simulationWrappers.get(0)) {
                out.println((t + 1) + ". " + wrapper.getAlgorithmWrapper().getDescription());
            }
        }
        if (isSortByUtility()) {
            out.println();
            out.println("Sorting by utility, high to low.");
        }
        if (isShowUtilities()) {
            out.println();
            out.println("Weighting of statistics:");
            out.println();
            out.println("U = ");
            for (Statistic stat : statistics.getStatistics()) {
                String statName = stat.getAbbreviation();
                double weight = statistics.getWeight(stat);
                if (weight != 0.0) {
                    out.println("    " + weight + " * f(" + statName + ")");
                }
            }
            out.println();
            out.println("...normed to range between 0 and 1.");
            out.println();
            out.println("Note that f for each statistic is a function that maps the statistic to the ");
            out.println("interval [0, 1], with higher being better.");
        }
        out.println();
        out.println("Graphs are being compared to the " + comparisonGraph.toString().replace("_", " ") + ".");
        out.println();
        // Add utilities to table as the last column.
        for (int u = 0; u < numTables; u++) {
            for (int t = 0; t < algorithmSimulationWrappers.size(); t++) {
                statTables[u][t][numStats] = utilities[t];
            }
        }
        // Print all of the tables.
        printStats(statTables, statistics, Mode.Average, newOrder, algorithmSimulationWrappers, algorithmWrappers, simulationWrappers, utilities, parameters);
        statTables = calcStatTables(allStats, Mode.StandardDeviation, numTables, algorithmSimulationWrappers, numStats, statistics);
        printStats(statTables, statistics, Mode.StandardDeviation, newOrder, algorithmSimulationWrappers, algorithmWrappers, simulationWrappers, utilities, parameters);
        statTables = calcStatTables(allStats, Mode.WorstCase, numTables, algorithmSimulationWrappers, numStats, statistics);
        // Add utilities to table as the last column.
        for (int u = 0; u < numTables; u++) {
            for (int t = 0; t < algorithmSimulationWrappers.size(); t++) {
                statTables[u][t][numStats] = utilities[t];
            }
        }
        printStats(statTables, statistics, Mode.WorstCase, newOrder, algorithmSimulationWrappers, algorithmWrappers, simulationWrappers, utilities, parameters);
    }
    out.close();
}