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edu.cmu.tetrad.simulation.HsimEvalFromData Maven / Gradle / Ivy
package edu.cmu.tetrad.simulation;
import edu.cmu.tetrad.data.CovarianceMatrix;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.ICovarianceMatrix;
import edu.cmu.tetrad.graph.Dag;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.GraphSaveLoadUtils;
import edu.cmu.tetrad.graph.GraphTransforms;
import edu.cmu.tetrad.search.Fges;
import edu.cmu.tetrad.search.score.SemBicScore;
import edu.cmu.tetrad.sem.SemEstimator;
import edu.cmu.tetrad.sem.SemIm;
import edu.cmu.tetrad.sem.SemPm;
import edu.cmu.tetrad.util.DataConvertUtils;
import edu.pitt.dbmi.data.reader.Delimiter;
import edu.pitt.dbmi.data.reader.tabular.ContinuousTabularDatasetFileReader;
import java.io.File;
import java.io.PrintWriter;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.*;
/**
* Created by ekummerfeld on 1/26/2017.
*/
public class HsimEvalFromData {
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;
final int iterations = 100;
int vars = 20;
int cases = 500;
int edgeratio = 3;
List hsimRepeat = Collections.singletonList(40);
List fsimRepeat = Collections.singletonList(40);
List[] fsimErrsByPars = new ArrayList[fsimRepeat.size()];
int whichFrepeat = 0;
for (int frepeat : fsimRepeat) {
fsimErrsByPars[whichFrepeat] = new ArrayList<>();
whichFrepeat++;
}
List[][] 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<>();
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 = GraphSaveLoadUtils.loadGraphTxt(new File("graph/graph.1.txt"));
Dag odag = new Dag(graph1);
Set eVars = new HashSet<>();
eVars.add("MULT");
Path dataFile = Paths.get("data/data.1.txt");
ContinuousTabularDatasetFileReader dataReader = new ContinuousTabularDatasetFileReader(dataFile, Delimiter.TAB);
data1 = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData(eVars));
vars = data1.getNumColumns();
cases = data1.getNumRows();
edgeratio = 3;
//!#@^$@&%^!#$!&@^ CALCULATING TARGET ERRORS $%$#@^@!%!#^$!%$#%
ICovarianceMatrix newcov = new CovarianceMatrix(data1);
SemBicScore oscore = new SemBicScore(newcov);
Fges ofgs = new Fges(oscore);
ofgs.setVerbose(false);
Graph oFGSGraph = ofgs.search();//***********This is the original FGS output on the data
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 errorsList = new ArrayList<>();
for (int r = 0; r < fsimRepeat.get(whichFrepeat); r++) {
Graph fgsDag = GraphTransforms.dagFromCPDAG(oFGSGraph, null);
Dag fgsdag2 = new Dag(fgsDag);
//then fit an IM to this dag and the data. GeneralizedSemEstimator seems to bug out
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 CovarianceMatrix(simData);
SemBicScore simscore = new SemBicScore(simcov);
Fges simfgs = new Fges(simscore);
simfgs.setVerbose(false);
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[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] = HsimEvalFromData.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] = HsimEvalFromData.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();
}
}
//******************Private Methods************************//
private static String[] prelimToPRAOtable(PRAOerrors input) {
String[] output = new String[5];
double[] values = input.toArray();
String[] vStrings = HsimUtils.formatErrorsArray(values, "%7.4e");
output[0] = input.getName();
System.arraycopy(vStrings, 0, output, 1, output.length - 1);
return output;
}
}
