Examples with TabularDataReader edu.pitt.dbmi.data.reader.tabular.TabularDataReader used on opensource projects

Example 1 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class DataLoaderSettings method loadDataWithSettings.

/**
 * Kevin's fast data reader
 *
 * @param file
 * @return DataModel on success
 */
public DataModel loadDataWithSettings(File file) throws IOException {
    DataModel dataModel = null;
    Delimiter delimiter = getDelimiterType();
    boolean hasHeader = isVarNamesFirstRow();
    String commentMarker = getCommentMarker();
    String missingValueMarker = getMissingValueMarker();
    if (tabularRadioButton.isSelected()) {
        TabularDataReader dataReader = null;
        // Continuous, discrete, mixed
        if (contRadioButton.isSelected()) {
            dataReader = new ContinuousTabularDataFileReader(file, delimiter);
        } else if (discRadioButton.isSelected()) {
            dataReader = new VerticalDiscreteTabularDataReader(file, delimiter);
        } else if (mixedRadioButton.isSelected()) {
            dataReader = new MixedTabularDataFileReader(getMaxNumOfDiscCategories(), file, delimiter);
        } else {
            throw new UnsupportedOperationException("Unsupported data type!");
        }
        // Header in first row or not
        dataReader.setHasHeader(hasHeader);
        // Set comment marker
        dataReader.setCommentMarker(commentMarker);
        dataReader.setMissingValueMarker(missingValueMarker);
        // Set the quote character
        if (doubleQuoteRadioButton.isSelected()) {
            dataReader.setQuoteCharacter('"');
        }
        if (singleQuoteRadioButton.isSelected()) {
            dataReader.setQuoteCharacter('\'');
        }
        Dataset dataset;
        // Handle case ID column based on different selections
        if (idNoneRadioButton.isSelected()) {
            // No column exclusion
            dataset = dataReader.readInData();
        } else if (idUnlabeledFirstColRadioButton.isSelected()) {
            // Exclude the first column
            dataset = dataReader.readInData(new int[] { 1 });
        } else if (idLabeledColRadioButton.isSelected() && !idStringField.getText().isEmpty()) {
            // Exclude the specified labled column
            dataset = dataReader.readInData(new HashSet<>(Arrays.asList(new String[] { idStringField.getText() })));
        } else {
            throw new UnsupportedOperationException("Unexpected 'Case ID column to ignore' selection.");
        }
        // Box Dataset to DataModel
        dataModel = DataConvertUtils.toDataModel(dataset);
    } else if (covarianceRadioButton.isSelected()) {
        // Covariance data can only be continuous
        CovarianceDataReader dataReader = new LowerCovarianceDataReader(file, delimiter);
        // Set comment marker
        dataReader.setCommentMarker(commentMarker);
        // Set the quote character
        if (doubleQuoteRadioButton.isSelected()) {
            dataReader.setQuoteCharacter('"');
        }
        if (singleQuoteRadioButton.isSelected()) {
            dataReader.setQuoteCharacter('\'');
        }
        Dataset dataset = dataReader.readInData();
        // Box Dataset to DataModel
        dataModel = DataConvertUtils.toDataModel(dataset);
    } else {
        throw new UnsupportedOperationException("Unsupported selection of File Type!");
    }
    return dataModel;
}

Example 2 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class GdistanceApply method main.

public static void main(String... args) {
    double xdist = 2.4;
    double ydist = 2.4;
    double zdist = 2;
    long timestart = System.nanoTime();
    System.out.println("Loading first graph");
    Graph graph1 = GraphUtils.loadGraphTxt(new File("Motion_Corrected_Graphs/singlesub_motion_graph_025_04.txt"));
    long timegraph1 = System.nanoTime();
    // System.out.println(graph1);
    System.out.println("Done loading first graph. Elapsed time: " + (timegraph1 - timestart) / 1000000000 + "s");
    System.out.println("Loading second graph");
    Graph graph2 = GraphUtils.loadGraphTxt(new File("Motion_Corrected_Graphs/singlesub_motion_graph_027_04.txt"));
    long timegraph2 = System.nanoTime();
    System.out.println("Done loading second graph. Elapsed time: " + (timegraph2 - timegraph1) / 1000000000 + "s");
    // +++++++++ these steps are specifically for the motion corrected fMRI graphs ++++++++++++
    graph1.removeNode(graph1.getNode("Motion_1"));
    graph1.removeNode(graph1.getNode("Motion_2"));
    graph1.removeNode(graph1.getNode("Motion_3"));
    graph1.removeNode(graph1.getNode("Motion_4"));
    graph1.removeNode(graph1.getNode("Motion_5"));
    graph1.removeNode(graph1.getNode("Motion_6"));
    graph2.removeNode(graph2.getNode("Motion_1"));
    graph2.removeNode(graph2.getNode("Motion_2"));
    graph2.removeNode(graph2.getNode("Motion_3"));
    graph2.removeNode(graph2.getNode("Motion_4"));
    graph2.removeNode(graph2.getNode("Motion_5"));
    graph2.removeNode(graph2.getNode("Motion_6"));
    // load the location map
    String workingDirectory = System.getProperty("user.dir");
    System.out.println(workingDirectory);
    Path mapPath = Paths.get("coords.txt");
    System.out.println(mapPath);
    TabularDataReader dataReaderMap = new ContinuousTabularDataFileReader(mapPath.toFile(), Delimiter.COMMA);
    try {
        DataSet locationMap = (DataSet) DataConvertUtils.toDataModel(dataReaderMap.readInData());
        long timegraph3 = System.nanoTime();
        System.out.println("Done loading location map. Elapsed time: " + (timegraph3 - timegraph2) / 1000000000 + "s");
        System.out.println("Running Gdistance");
        Gdistance gdist = new Gdistance(locationMap, xdist, ydist, zdist);
        List<Double> distance = gdist.distances(graph1, graph2);
        System.out.println(distance);
        System.out.println("Done running Distance. Elapsed time: " + (System.nanoTime() - timegraph3) / 1000000000 + "s");
        System.out.println("Total elapsed time: " + (System.nanoTime() - timestart) / 1000000000 + "s");
        PrintWriter writer = new PrintWriter("Gdistances.txt", "UTF-8");
        writer.println(distance);
        writer.close();
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}

Example 3 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class GdistanceTest method main.

public static void main(String... args) {
    // first generate a couple random graphs
    int numVars = 16;
    int numEdges = 16;
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    Graph testdag1 = GraphUtils.randomGraphRandomForwardEdges(vars, 0, numEdges, 30, 15, 15, false, true);
    Graph testdag2 = GraphUtils.randomGraphRandomForwardEdges(vars, 0, numEdges, 30, 15, 15, false, true);
    // System.out.println(testdag1);
    // load the location map
    String workingDirectory = System.getProperty("user.dir");
    System.out.println(workingDirectory);
    Path mapPath = Paths.get("locationMap.txt");
    System.out.println(mapPath);
    TabularDataReader dataReaderMap = new ContinuousTabularDataFileReader(mapPath.toFile(), Delimiter.COMMA);
    try {
        DataSet locationMap = (DataSet) DataConvertUtils.toDataModel(dataReaderMap.readInData());
        // System.out.println(locationMap);
        // then compare their distance
        double xdist = 2.4;
        double ydist = 2.4;
        double zdist = 2;
        Gdistance gdist = new Gdistance(locationMap, xdist, ydist, zdist);
        List<Double> output = gdist.distances(testdag1, testdag2);
        System.out.println(output);
        PrintWriter writer = new PrintWriter("Gdistances.txt", "UTF-8");
        writer.println(output);
        writer.close();
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}

Example 4 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class HsimEvalFromData method main.

public static void main(String[] args) {
    long timestart = System.nanoTime();
    System.out.println("Beginning Evaluation");
    String nl = System.lineSeparator();
    String output = "Simulation edu.cmu.tetrad.study output comparing Fsim and Hsim on predicting graph discovery accuracy" + nl;
    int iterations = 100;
    int vars = 20;
    int cases = 500;
    int edgeratio = 3;
    List<Integer> hsimRepeat = Arrays.asList(40);
    List<Integer> fsimRepeat = Arrays.asList(40);
    List<PRAOerrors>[] fsimErrsByPars = new ArrayList[fsimRepeat.size()];
    int whichFrepeat = 0;
    for (int frepeat : fsimRepeat) {
        fsimErrsByPars[whichFrepeat] = new ArrayList<PRAOerrors>();
        whichFrepeat++;
    }
    List<PRAOerrors>[][] hsimErrsByPars = new ArrayList[1][hsimRepeat.size()];
    // System.out.println(resimSize.size()+" "+hsimRepeat.size());
    int whichHrepeat;
    whichHrepeat = 0;
    for (int hrepeat : hsimRepeat) {
        // System.out.println(whichrsize+" "+whichHrepeat);
        hsimErrsByPars[0][whichHrepeat] = new ArrayList<PRAOerrors>();
        whichHrepeat++;
    }
    // !(*%(@!*^!($%!^ START ITERATING HERE !#$%(*$#@!^(*!$*%(!$#
    try {
        for (int iterate = 0; iterate < iterations; iterate++) {
            System.out.println("iteration " + iterate);
            // @#$%@$%^@$^@$^@%$%@$#^ LOADING THE DATA AND GRAPH @$#%%*#^##*^$#@%$
            DataSet data1;
            Graph graph1 = GraphUtils.loadGraphTxt(new File("graph/graph.1.txt"));
            Dag odag = new Dag(graph1);
            Set<String> eVars = new HashSet<String>();
            eVars.add("MULT");
            Path dataFile = Paths.get("data/data.1.txt");
            TabularDataReader dataReader = new ContinuousTabularDataFileReader(dataFile.toFile(), Delimiter.TAB);
            data1 = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData(eVars));
            vars = data1.getNumColumns();
            cases = data1.getNumRows();
            edgeratio = 3;
            // !#@^$@&%^!#$!&@^ CALCULATING TARGET ERRORS $%$#@^@!%!#^$!%$#%
            ICovarianceMatrix newcov = new CovarianceMatrixOnTheFly(data1);
            SemBicScore oscore = new SemBicScore(newcov);
            Fges ofgs = new Fges(oscore);
            ofgs.setVerbose(false);
            ofgs.setNumPatternsToStore(0);
            // ***********This is the original FGS output on the data
            Graph oFGSGraph = ofgs.search();
            PRAOerrors oErrors = new PRAOerrors(HsimUtils.errorEval(oFGSGraph, odag), "target errors");
            // **then step 1: full resim. iterate through the combinations of estimator parameters (just repeat num)
            for (whichFrepeat = 0; whichFrepeat < fsimRepeat.size(); whichFrepeat++) {
                ArrayList<PRAOerrors> errorsList = new ArrayList<PRAOerrors>();
                for (int r = 0; r < fsimRepeat.get(whichFrepeat); r++) {
                    PatternToDag pickdag = new PatternToDag(oFGSGraph);
                    Graph fgsDag = pickdag.patternToDagMeek();
                    Dag fgsdag2 = new Dag(fgsDag);
                    // then fit an IM to this dag and the data. GeneralizedSemEstimator seems to bug out
                    // GeneralizedSemPm simSemPm = new GeneralizedSemPm(fgsdag2);
                    // GeneralizedSemEstimator gsemEstimator = new GeneralizedSemEstimator();
                    // GeneralizedSemIm fittedIM = gsemEstimator.estimate(simSemPm, oData);
                    SemPm simSemPm = new SemPm(fgsdag2);
                    // BayesPm simBayesPm = new BayesPm(fgsdag2, bayesPm);
                    SemEstimator simSemEstimator = new SemEstimator(data1, simSemPm);
                    SemIm fittedIM = simSemEstimator.estimate();
                    DataSet simData = fittedIM.simulateData(data1.getNumRows(), false);
                    // after making the full resim data (simData), run FGS on that
                    ICovarianceMatrix simcov = new CovarianceMatrixOnTheFly(simData);
                    SemBicScore simscore = new SemBicScore(simcov);
                    Fges simfgs = new Fges(simscore);
                    simfgs.setVerbose(false);
                    simfgs.setNumPatternsToStore(0);
                    Graph simGraphOut = simfgs.search();
                    PRAOerrors simErrors = new PRAOerrors(HsimUtils.errorEval(simGraphOut, fgsdag2), "Fsim errors " + r);
                    errorsList.add(simErrors);
                }
                PRAOerrors avErrors = new PRAOerrors(errorsList, "Average errors for Fsim at repeat=" + fsimRepeat.get(whichFrepeat));
                // if (verbosity>3) System.out.println(avErrors.allToString());
                // ****calculate the squared errors of prediction, store all these errors in a list
                double FsimAR2 = (avErrors.getAdjRecall() - oErrors.getAdjRecall()) * (avErrors.getAdjRecall() - oErrors.getAdjRecall());
                double FsimAP2 = (avErrors.getAdjPrecision() - oErrors.getAdjPrecision()) * (avErrors.getAdjPrecision() - oErrors.getAdjPrecision());
                double FsimOR2 = (avErrors.getOrientRecall() - oErrors.getOrientRecall()) * (avErrors.getOrientRecall() - oErrors.getOrientRecall());
                double FsimOP2 = (avErrors.getOrientPrecision() - oErrors.getOrientPrecision()) * (avErrors.getOrientPrecision() - oErrors.getOrientPrecision());
                PRAOerrors Fsim2 = new PRAOerrors(new double[] { FsimAR2, FsimAP2, FsimOR2, FsimOP2 }, "squared errors for Fsim at repeat=" + fsimRepeat.get(whichFrepeat));
                // add the fsim squared errors to the appropriate list
                fsimErrsByPars[whichFrepeat].add(Fsim2);
            }
            // **then step 2: hybrid sim. iterate through combos of params (repeat num, resimsize)
            for (whichHrepeat = 0; whichHrepeat < hsimRepeat.size(); whichHrepeat++) {
                HsimRepeatAC study = new HsimRepeatAC(data1);
                PRAOerrors HsimErrors = new PRAOerrors(study.run(1, hsimRepeat.get(whichHrepeat)), "Hsim errors" + "at rsize=" + 1 + " repeat=" + hsimRepeat.get(whichHrepeat));
                // ****calculate the squared errors of prediction
                double HsimAR2 = (HsimErrors.getAdjRecall() - oErrors.getAdjRecall()) * (HsimErrors.getAdjRecall() - oErrors.getAdjRecall());
                double HsimAP2 = (HsimErrors.getAdjPrecision() - oErrors.getAdjPrecision()) * (HsimErrors.getAdjPrecision() - oErrors.getAdjPrecision());
                double HsimOR2 = (HsimErrors.getOrientRecall() - oErrors.getOrientRecall()) * (HsimErrors.getOrientRecall() - oErrors.getOrientRecall());
                double HsimOP2 = (HsimErrors.getOrientPrecision() - oErrors.getOrientPrecision()) * (HsimErrors.getOrientPrecision() - oErrors.getOrientPrecision());
                PRAOerrors Hsim2 = new PRAOerrors(new double[] { HsimAR2, HsimAP2, HsimOR2, HsimOP2 }, "squared errors for Hsim, rsize=" + 1 + " repeat=" + hsimRepeat.get(whichHrepeat));
                hsimErrsByPars[0][whichHrepeat].add(Hsim2);
            }
        }
        // Average the squared errors for each set of fsim/hsim params across all iterations
        PRAOerrors[] fMSE = new PRAOerrors[fsimRepeat.size()];
        PRAOerrors[][] hMSE = new PRAOerrors[1][hsimRepeat.size()];
        String[][] latexTableArray = new String[1 * hsimRepeat.size() + fsimRepeat.size()][5];
        for (int j = 0; j < fMSE.length; j++) {
            fMSE[j] = new PRAOerrors(fsimErrsByPars[j], "MSE for Fsim at vars=" + vars + " edgeratio=" + edgeratio + " cases=" + cases + " frepeat=" + fsimRepeat.get(j) + " iterations=" + iterations);
            // if(verbosity>0){System.out.println(fMSE[j].allToString());}
            output = output + fMSE[j].allToString() + nl;
            latexTableArray[j] = prelimToPRAOtable(fMSE[j]);
        }
        for (int j = 0; j < hMSE.length; j++) {
            for (int k = 0; k < hMSE[j].length; k++) {
                hMSE[j][k] = new PRAOerrors(hsimErrsByPars[j][k], "MSE for Hsim at vars=" + vars + " edgeratio=" + edgeratio + " cases=" + cases + " rsize=" + 1 + " repeat=" + hsimRepeat.get(k) + " iterations=" + iterations);
                // if(verbosity>0){System.out.println(hMSE[j][k].allToString());}
                output = output + hMSE[j][k].allToString() + nl;
                latexTableArray[fsimRepeat.size() + j * hMSE[j].length + k] = prelimToPRAOtable(hMSE[j][k]);
            }
        }
        // record all the params, the base error values, and the fsim/hsim mean squared errors
        String latexTable = HsimUtils.makeLatexTable(latexTableArray);
        PrintWriter writer = new PrintWriter("latexTable.txt", "UTF-8");
        writer.println(latexTable);
        writer.close();
        PrintWriter writer2 = new PrintWriter("HvsF-SimulationEvaluation.txt", "UTF-8");
        writer2.println(output);
        writer2.close();
        long timestop = System.nanoTime();
        System.out.println("Evaluation Concluded. Duration: " + (timestop - timestart) / 1000000000 + "s");
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}

Example 5 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class HsimRun method run.

public static void run(String readfilename, String filenameOut, char delimiter, String[] resimNodeNames, boolean verbose) {
    // ===========read data from file=============
    String workingDirectory = System.getProperty("user.dir");
    System.out.println(workingDirectory);
    try {
        Path dataFile = Paths.get(readfilename);
        TabularDataReader dataReader = new VerticalDiscreteTabularDataReader(dataFile.toFile(), DelimiterUtils.toDelimiter(delimiter));
        DataSet dataSet = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData());
        System.out.println("cols: " + dataSet.getNumColumns() + " rows: " + dataSet.getNumRows());
        // testing the read file
        // DataWriter.writeRectangularData(dataSet, new FileWriter("dataOut2.txt"), '\t');
        // apply Hsim to data, with whatever parameters
        // ========first make the Dag for Hsim==========
        // ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(dataSet);
        double penaltyDiscount = 2.0;
        SemBicScore score = new SemBicScore(new CovarianceMatrixOnTheFly(dataSet));
        score.setPenaltyDiscount(penaltyDiscount);
        Fges fges = new Fges(score);
        fges.setVerbose(false);
        fges.setNumPatternsToStore(0);
        // fges.setCorrErrorsAlpha(penaltyDiscount);
        // fges.setOut(out);
        // fges.setFaithfulnessAssumed(true);
        // fges.setMaxIndegree(1);
        // fges.setCycleBound(5);
        Graph estGraph = fges.search();
        System.out.println(estGraph);
        Graph estPattern = new EdgeListGraphSingleConnections(estGraph);
        PatternToDag patternToDag = new PatternToDag(estPattern);
        Graph estGraphDAG = patternToDag.patternToDagMeek();
        Dag estDAG = new Dag(estGraphDAG);
        // ===========Identify the nodes to be resimulated===========
        // estDAG.getNodes()
        // need to populate simnodes with the nodes to be resimulated
        // for now, I choose a center Node and add its neighbors
        /* ===Commented out, but saved for future use=====
            Node centerNode = estDAG.getNode("X3");
            Set<Node> simnodes = new HashSet<Node>();
            simnodes.add(centerNode);
            simnodes.addAll(estDAG.getAdjacentNodes(centerNode));
             */
        // ===test code, for user input specifying specific set of resim nodes====
        // user needs to specify a list or array or something of node names
        // use for loop through that collection, get each node from the names, add to the set
        Set<Node> simnodes = new HashSet<>();
        for (int i = 0; i < resimNodeNames.length; i++) {
            Node thisNode = estDAG.getNode(resimNodeNames[i]);
            simnodes.add(thisNode);
        }
        // ===========Apply the hybrid resimulation===============
        Hsim hsim = new Hsim(estDAG, simnodes, dataSet);
        DataSet newDataSet = hsim.hybridsimulate();
        // write output to a new file
        DataWriter.writeRectangularData(newDataSet, new FileWriter(filenameOut), delimiter);
        // =======Run FGES on the output data, and compare it to the original learned graph
        Path dataFileOut = Paths.get(filenameOut);
        TabularDataReader dataReaderOut = new VerticalDiscreteTabularDataReader(dataFileOut.toFile(), DelimiterUtils.toDelimiter(delimiter));
        DataSet dataSetOut = (DataSet) DataConvertUtils.toDataModel(dataReaderOut.readInData());
        SemBicScore _score = new SemBicScore(new CovarianceMatrix(dataSetOut));
        _score.setPenaltyDiscount(2.0);
        Fges fgesOut = new Fges(_score);
        fgesOut.setVerbose(false);
        fgesOut.setNumPatternsToStore(0);
        // fgesOut.setCorrErrorsAlpha(2.0);
        // fgesOut.setOut(out);
        // fgesOut.setFaithfulnessAssumed(true);
        // fgesOut.setMaxIndegree(1);
        // fgesOut.setCycleBound(5);
        Graph estGraphOut = fgesOut.search();
        System.out.println(estGraphOut);
        SearchGraphUtils.graphComparison(estGraphOut, estGraph, System.out);
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}