Examples with Model dr.inference.model.Model used on opensource projects

Example 6 with Model

use of dr.inference.model.Model in project beast-mcmc by beast-dev.

the class CheckPointModifier method readStateFromFile.

private long readStateFromFile(File file, MarkovChain markovChain, double[] lnL) {
    OperatorSchedule operatorSchedule = markovChain.getSchedule();
    long state = -1;
    this.traitModels = new ArrayList<TreeParameterModel>();
    try {
        FileReader fileIn = new FileReader(file);
        BufferedReader in = new BufferedReader(fileIn);
        int[] rngState = null;
        String line = in.readLine();
        String[] fields = line.split("\t");
        if (fields[0].equals("rng")) {
            // if there is a random number generator state present then load it...
            try {
                rngState = new int[fields.length - 1];
                for (int i = 0; i < rngState.length; i++) {
                    rngState[i] = Integer.parseInt(fields[i + 1]);
                }
            } catch (NumberFormatException nfe) {
                throw new RuntimeException("Unable to read state number from state file");
            }
            line = in.readLine();
            fields = line.split("\t");
        }
        try {
            if (!fields[0].equals("state")) {
                throw new RuntimeException("Unable to read state number from state file");
            }
            state = Long.parseLong(fields[1]);
        } catch (NumberFormatException nfe) {
            throw new RuntimeException("Unable to read state number from state file");
        }
        line = in.readLine();
        fields = line.split("\t");
        try {
            if (!fields[0].equals("lnL")) {
                throw new RuntimeException("Unable to read lnL from state file");
            }
            if (lnL != null) {
                lnL[0] = Double.parseDouble(fields[1]);
            }
        } catch (NumberFormatException nfe) {
            throw new RuntimeException("Unable to read lnL from state file");
        }
        line = in.readLine();
        //System.out.println(line);
        fields = line.split("\t");
        //Tree nodes have numbers as parameter ids
        for (Parameter parameter : Parameter.CONNECTED_PARAMETER_SET) {
            //numbers should be positive but can include zero
            if (isTreeNode(parameter.getId()) && isTreeNode(fields[1]) || parameter.getId().equals(fields[1])) {
                int dimension = Integer.parseInt(fields[2]);
                if (dimension != parameter.getDimension() && !fields[1].equals("branchRates.categories")) {
                    System.err.println("Unable to match state parameter dimension: " + dimension + ", expecting " + parameter.getDimension() + " for parameter: " + parameter.getParameterName());
                    System.err.print("Read from file: ");
                    for (int i = 0; i < fields.length; i++) {
                        System.err.print(fields[i] + "\t");
                    }
                    System.err.println();
                }
                if (fields[1].equals("branchRates.categories.rootNodeNumber")) {
                    // System.out.println("eek");
                    double value = Double.parseDouble(fields[3]);
                    parameter.setParameterValue(0, value);
                    if (DEBUG) {
                        System.out.println("restoring " + fields[1] + " with value " + value);
                    }
                } else {
                    if (DEBUG) {
                        System.out.print("restoring " + fields[1] + " with values ");
                    }
                    if (fields[1].equals("branchRates.categories")) {
                        for (int dim = 0; dim < (fields.length - 3); dim++) {
                            //System.out.println("dim " + dim);
                            parameter.setParameterValue(dim, Double.parseDouble(fields[dim + 3]));
                            if (DEBUG) {
                                System.out.print(Double.parseDouble(fields[dim + 3]) + " ");
                            }
                        }
                    } else {
                        for (int dim = 0; dim < parameter.getDimension(); dim++) {
                            parameter.setParameterValue(dim, Double.parseDouble(fields[dim + 3]));
                            if (DEBUG) {
                                System.out.print(Double.parseDouble(fields[dim + 3]) + " ");
                            }
                        }
                    }
                    if (DEBUG) {
                        System.out.println();
                    }
                }
                line = in.readLine();
                //System.out.println(line);
                fields = line.split("\t");
            } else {
            //there will be more parameters in the connected set than there are lines in the checkpoint file
            //do nothing and just keep iterating over the parameters in the connected set
            }
        }
        //No changes needed for loading in operators
        for (int i = 0; i < operatorSchedule.getOperatorCount(); i++) {
            MCMCOperator operator = operatorSchedule.getOperator(i);
            if (!fields[1].equals(operator.getOperatorName())) {
                throw new RuntimeException("Unable to match operator: " + fields[1]);
            }
            if (fields.length < 4) {
                throw new RuntimeException("Operator missing values: " + fields[1]);
            }
            operator.setAcceptCount(Integer.parseInt(fields[2]));
            operator.setRejectCount(Integer.parseInt(fields[3]));
            if (operator instanceof CoercableMCMCOperator) {
                if (fields.length != 5) {
                    throw new RuntimeException("Coercable operator missing parameter: " + fields[1]);
                }
                ((CoercableMCMCOperator) operator).setCoercableParameter(Double.parseDouble(fields[4]));
            }
            line = in.readLine();
            fields = line.split("\t");
        }
        // load the tree models last as we get the node heights from the tree (not the parameters which
        // which may not be associated with the right node
        Set<String> expectedTreeModelNames = new HashSet<String>();
        for (Model model : Model.CONNECTED_MODEL_SET) {
            if (model instanceof TreeModel) {
                expectedTreeModelNames.add(model.getModelName());
            }
            if (model instanceof TreeParameterModel) {
                this.traitModels.add((TreeParameterModel) model);
            }
            if (model instanceof BranchRates) {
                this.rateModel = (BranchRates) model;
            }
        }
        while (fields[0].equals("tree")) {
            for (Model model : Model.CONNECTED_MODEL_SET) {
                if (model instanceof TreeModel && fields[1].equals(model.getModelName())) {
                    //AR: Can we not just add them to a Flexible tree and then make a new TreeModel
                    //taking that in the constructor?
                    //internally, we have a tree with all the taxa
                    //externally, i.e. in the checkpoint file, we have a tree representation comprising
                    //a subset of the full taxa set
                    //write method that adjusts the internal representation, i.e. the one in the connected
                    //set, according to the checkpoint file and a distance-based approach to position
                    //the additional taxa
                    //first read in all the data from the checkpoint file
                    line = in.readLine();
                    line = in.readLine();
                    fields = line.split("\t");
                    //read number of nodes
                    int nodeCount = Integer.parseInt(fields[0]);
                    double[] nodeHeights = new double[nodeCount];
                    String[] taxaNames = new String[(nodeCount + 1) / 2];
                    for (int i = 0; i < nodeCount; i++) {
                        line = in.readLine();
                        fields = line.split("\t");
                        nodeHeights[i] = Double.parseDouble(fields[1]);
                        if (i < taxaNames.length) {
                            taxaNames[i] = fields[2];
                        }
                    }
                    //on to reading edge information
                    line = in.readLine();
                    line = in.readLine();
                    line = in.readLine();
                    fields = line.split("\t");
                    int edgeCount = Integer.parseInt(fields[0]);
                    //create data matrix of doubles to store information from list of TreeParameterModels
                    double[][] traitValues = new double[traitModels.size()][edgeCount];
                    //create array to store whether a node is left or right child of its parent
                    //can be important for certain tree transition kernels
                    int[] childOrder = new int[edgeCount];
                    for (int i = 0; i < childOrder.length; i++) {
                        childOrder[i] = -1;
                    }
                    int[] parents = new int[edgeCount];
                    for (int i = 0; i < edgeCount; i++) {
                        parents[i] = -1;
                    }
                    for (int i = 0; i < edgeCount; i++) {
                        line = in.readLine();
                        if (line != null) {
                            fields = line.split("\t");
                            parents[Integer.parseInt(fields[0])] = Integer.parseInt(fields[1]);
                            childOrder[i] = Integer.parseInt(fields[2]);
                            for (int j = 0; j < traitModels.size(); j++) {
                                traitValues[j][i] = Double.parseDouble(fields[3 + j]);
                            }
                        }
                    }
                    //perform magic with the acquired information
                    //CheckPointTreeModifier modifyTree = new CheckPointTreeModifier((TreeModel) model);
                    this.modifyTree = new CheckPointTreeModifier((TreeModel) model);
                    modifyTree.adoptTreeStructure(parents, nodeHeights, childOrder, taxaNames);
                    if (traitModels.size() > 0) {
                        modifyTree.adoptTraitData(parents, this.traitModels, traitValues);
                    }
                    //adopt the loaded tree structure; this does not yet copy the traits on the branches
                    //((TreeModel) model).beginTreeEdit();
                    //((TreeModel) model).adoptTreeStructure(parents, nodeHeights, childOrder);
                    //((TreeModel) model).endTreeEdit();
                    expectedTreeModelNames.remove(model.getModelName());
                }
            }
            line = in.readLine();
            if (line != null) {
                fields = line.split("\t");
            }
        }
        if (expectedTreeModelNames.size() > 0) {
            StringBuilder sb = new StringBuilder();
            for (String notFoundName : expectedTreeModelNames) {
                sb.append("Expecting, but unable to match state parameter:" + notFoundName + "\n");
            }
            throw new RuntimeException(sb.toString());
        }
        in.close();
        fileIn.close();
    } catch (IOException ioe) {
        throw new RuntimeException("Unable to read file: " + ioe.getMessage());
    }
    return state;
}

Example 7 with Model

use of dr.inference.model.Model in project beast-mcmc by beast-dev.

the class MCMCParser method parseXMLObject.

/**
     * @return an mcmc object based on the XML element it was passed.
     */
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    MCMC mcmc = new MCMC(xo.getAttribute(NAME, "mcmc1"));
    long chainLength = xo.getLongIntegerAttribute(CHAIN_LENGTH);
    boolean useCoercion = xo.getAttribute(COERCION, true);
    long coercionDelay = chainLength / 100;
    if (xo.hasAttribute(PRE_BURNIN)) {
        coercionDelay = xo.getIntegerAttribute(PRE_BURNIN);
    }
    coercionDelay = xo.getAttribute(COERCION_DELAY, coercionDelay);
    double temperature = xo.getAttribute(TEMPERATURE, 1.0);
    long fullEvaluationCount = xo.getAttribute(FULL_EVALUATION, 2000);
    double evaluationTestThreshold = MarkovChain.EVALUATION_TEST_THRESHOLD;
    if (System.getProperty("mcmc.evaluation.threshold") != null) {
        evaluationTestThreshold = Double.parseDouble(System.getProperty("mcmc.evaluation.threshold"));
    }
    evaluationTestThreshold = xo.getAttribute(EVALUATION_THRESHOLD, evaluationTestThreshold);
    int minOperatorCountForFullEvaluation = xo.getAttribute(MIN_OPS_EVALUATIONS, 1);
    MCMCOptions options = new MCMCOptions(chainLength, fullEvaluationCount, minOperatorCountForFullEvaluation, evaluationTestThreshold, useCoercion, coercionDelay, temperature);
    OperatorSchedule opsched = (OperatorSchedule) xo.getChild(OperatorSchedule.class);
    Likelihood likelihood = (Likelihood) xo.getChild(Likelihood.class);
    likelihood.setUsed();
    if (Boolean.valueOf(System.getProperty("show_warnings", "false"))) {
        // check that all models, parameters and likelihoods are being used
        for (Likelihood l : Likelihood.FULL_LIKELIHOOD_SET) {
            if (!l.isUsed()) {
                java.util.logging.Logger.getLogger("dr.inference").warning("Likelihood, " + l.getId() + ", of class " + l.getClass().getName() + " is not being handled by the MCMC.");
            }
        }
        for (Model m : Model.FULL_MODEL_SET) {
            if (!m.isUsed()) {
                java.util.logging.Logger.getLogger("dr.inference").warning("Model, " + m.getId() + ", of class " + m.getClass().getName() + " is not being handled by the MCMC.");
            }
        }
        for (Parameter p : Parameter.FULL_PARAMETER_SET) {
            if (!p.isUsed()) {
                java.util.logging.Logger.getLogger("dr.inference").warning("Parameter, " + p.getId() + ", of class " + p.getClass().getName() + " is not being handled by the MCMC.");
            }
        }
    }
    ArrayList<Logger> loggers = new ArrayList<Logger>();
    for (int i = 0; i < xo.getChildCount(); i++) {
        Object child = xo.getChild(i);
        if (child instanceof Logger) {
            loggers.add((Logger) child);
        }
    }
    mcmc.setShowOperatorAnalysis(true);
    if (xo.hasAttribute(OPERATOR_ANALYSIS)) {
        mcmc.setOperatorAnalysisFile(XMLParser.getLogFile(xo, OPERATOR_ANALYSIS));
    }
    Logger[] loggerArray = new Logger[loggers.size()];
    loggers.toArray(loggerArray);
    java.util.logging.Logger.getLogger("dr.inference").info("\nCreating the MCMC chain:" + "\n  chainLength=" + options.getChainLength() + "\n  autoOptimize=" + options.useCoercion() + (options.useCoercion() ? "\n  autoOptimize delayed for " + options.getCoercionDelay() + " steps" : "") + (options.getFullEvaluationCount() == 0 ? "\n  full evaluation test off" : ""));
    mcmc.init(options, likelihood, opsched, loggerArray);
    MarkovChain mc = mcmc.getMarkovChain();
    double initialScore = mc.getCurrentScore();
    if (initialScore == Double.NEGATIVE_INFINITY) {
        String message = "The initial posterior is zero";
        if (likelihood instanceof CompoundLikelihood) {
            message += ": " + ((CompoundLikelihood) likelihood).getDiagnosis(2);
        } else {
            message += "!";
        }
        throw new IllegalArgumentException(message);
    }
    if (!xo.getAttribute(SPAWN, true))
        mcmc.setSpawnable(false);
    return mcmc;
}