Ejemplo n.º 1
0
  public static void test() throws Exception {
    ServletContextParameterMap contextParameters =
        ContextParametersRegistry.getInstance().getDefault();
    ModelingConfiguration modelingConfiguration =
        ModelingConfigurationRegistry.getInstance()
            .getModelingConfiguration(contextParameters.getId());

    //		String inputPath = Params.INPUT_DIR;
    String outputPath = Params.OUTPUT_DIR;
    String graphPath = Params.GRAPHS_DIR;

    //		List<SemanticModel> semanticModels = ModelReader.importSemanticModels(inputPath);
    List<SemanticModel> semanticModels =
        ModelReader.importSemanticModelsFromJsonFiles(Params.MODEL_DIR, Params.MODEL_MAIN_FILE_EXT);

    //		ModelEvaluation me2 = semanticModels.get(20).evaluate(semanticModels.get(20));
    //		System.out.println(me2.getPrecision() + "--" + me2.getRecall());
    //		if (true)
    //			return;

    List<SemanticModel> trainingData = new ArrayList<SemanticModel>();

    OntologyManager ontologyManager = new OntologyManager(contextParameters.getId());
    File ff = new File(Params.ONTOLOGY_DIR);
    File[] files = ff.listFiles();
    for (File f : files) {
      ontologyManager.doImport(f, "UTF-8");
    }
    ontologyManager.updateCache();

    //		getStatistics1(semanticModels);

    //		if (true)
    //			return;

    ModelLearningGraph modelLearningGraph = null;

    ModelLearner_Old modelLearner;

    boolean iterativeEvaluation = false;
    boolean useCorrectType = false;
    int numberOfCRFCandidates = 4;
    int numberOfKnownModels;
    String filePath = Params.RESULTS_DIR;
    String filename = "results,k=" + numberOfCRFCandidates + ".csv";
    PrintWriter resultFile = new PrintWriter(new File(filePath + filename));

    StringBuffer[] resultsArray = new StringBuffer[semanticModels.size() + 2];
    for (int i = 0; i < resultsArray.length; i++) {
      resultsArray[i] = new StringBuffer();
    }

    for (int i = 0; i < semanticModels.size(); i++) {
      //		for (int i = 0; i <= 10; i++) {
      //		int i = 3; {

      resultFile.flush();
      int newSourceIndex = i;
      SemanticModel newSource = semanticModels.get(newSourceIndex);

      logger.info("======================================================");
      logger.info(newSource.getName() + "(#attributes:" + newSource.getColumnNodes().size() + ")");
      System.out.println(
          newSource.getName() + "(#attributes:" + newSource.getColumnNodes().size() + ")");
      logger.info("======================================================");

      if (!iterativeEvaluation) numberOfKnownModels = semanticModels.size() - 1;
      else numberOfKnownModels = 0;

      if (resultsArray[0].length() > 0) resultsArray[0].append(" \t ");
      resultsArray[0].append(
          newSource.getName()
              + "("
              + newSource.getColumnNodes().size()
              + ")"
              + "\t"
              + " "
              + "\t"
              + " ");
      if (resultsArray[1].length() > 0) resultsArray[1].append(" \t ");
      resultsArray[1].append("p \t r \t t");

      while (numberOfKnownModels <= semanticModels.size() - 1) {

        trainingData.clear();

        int j = 0, count = 0;
        while (count < numberOfKnownModels) {
          if (j != newSourceIndex) {
            trainingData.add(semanticModels.get(j));
            count++;
          }
          j++;
        }

        modelLearningGraph =
            (ModelLearningGraphSparse)
                ModelLearningGraph.getEmptyInstance(ontologyManager, ModelLearningGraphType.Sparse);

        SemanticModel correctModel = newSource;
        List<ColumnNode> columnNodes = correctModel.getColumnNodes();
        //				if (useCorrectType && numberOfCRFCandidates > 1)
        //					updateCrfSemanticTypesForResearchEvaluation(columnNodes);

        modelLearner = new ModelLearner_Old(ontologyManager, columnNodes);
        long start = System.currentTimeMillis();

        String graphName =
            !iterativeEvaluation
                ? graphPath + semanticModels.get(newSourceIndex).getName() + Params.GRAPH_FILE_EXT
                : graphPath
                    + semanticModels.get(newSourceIndex).getName()
                    + ".knownModels="
                    + numberOfKnownModels
                    + Params.GRAPH_FILE_EXT;

        if (new File(graphName).exists()) {
          // read graph from file
          try {
            logger.info("loading the graph ...");
            DirectedWeightedMultigraph<Node, DefaultLink> graph = GraphUtil.importJson(graphName);
            modelLearner.graphBuilder = new GraphBuilder(ontologyManager, graph, false);
            modelLearner.nodeIdFactory = modelLearner.graphBuilder.getNodeIdFactory();
          } catch (Exception e) {
            e.printStackTrace();
          }
        } else {
          logger.info("building the graph ...");
          for (SemanticModel sm : trainingData) modelLearningGraph.addModel(sm, false);
          modelLearner.graphBuilder = modelLearningGraph.getGraphBuilder();
          modelLearner.nodeIdFactory = modelLearner.graphBuilder.getNodeIdFactory();
          // save graph to file
          try {
            GraphUtil.exportJson(
                modelLearningGraph.getGraphBuilder().getGraph(), graphName, true, true);
          } catch (Exception e) {
            e.printStackTrace();
          }
        }

        List<SortableSemanticModel_Old> hypothesisList =
            modelLearner.hypothesize(useCorrectType, numberOfCRFCandidates);

        long elapsedTimeMillis = System.currentTimeMillis() - start;
        float elapsedTimeSec = elapsedTimeMillis / 1000F;

        List<SortableSemanticModel_Old> topHypotheses = null;
        if (hypothesisList != null) {

          topHypotheses =
              hypothesisList.size() > modelingConfiguration.getNumCandidateMappings()
                  ? hypothesisList.subList(0, modelingConfiguration.getNumCandidateMappings())
                  : hypothesisList;
        }

        Map<String, SemanticModel> models = new TreeMap<String, SemanticModel>();

        // export to json
        //				if (topHypotheses != null)
        //					for (int k = 0; k < topHypotheses.size() && k < 3; k++) {
        //
        //						String fileExt = null;
        //						if (k == 0) fileExt = Params.MODEL_RANK1_FILE_EXT;
        //						else if (k == 1) fileExt = Params.MODEL_RANK2_FILE_EXT;
        //						else if (k == 2) fileExt = Params.MODEL_RANK3_FILE_EXT;
        //						SortableSemanticModel m = topHypotheses.get(k);
        //						new SemanticModel(m).writeJson(Params.MODEL_DIR +
        //								newSource.getName() + fileExt);
        //
        //					}

        ModelEvaluation me;
        models.put("1-correct model", correctModel);
        if (topHypotheses != null)
          for (int k = 0; k < topHypotheses.size(); k++) {

            SortableSemanticModel_Old m = topHypotheses.get(k);

            me = m.evaluate(correctModel);

            String label =
                "candidate"
                    + k
                    + m.getSteinerNodes().getScoreDetailsString()
                    + "cost:"
                    + roundTwoDecimals(m.getCost())
                    +
                    //								"-distance:" + me.getDistance() +
                    "-precision:"
                    + me.getPrecision()
                    + "-recall:"
                    + me.getRecall();

            models.put(label, m);

            if (k == 0) { // first rank model
              System.out.println(
                  "number of known models: "
                      + numberOfKnownModels
                      + ", precision: "
                      + me.getPrecision()
                      + ", recall: "
                      + me.getRecall()
                      + ", time: "
                      + elapsedTimeSec);
              logger.info(
                  "number of known models: "
                      + numberOfKnownModels
                      + ", precision: "
                      + me.getPrecision()
                      + ", recall: "
                      + me.getRecall()
                      + ", time: "
                      + elapsedTimeSec);
              //							resultFile.println("number of known models \t precision \t recall");
              //							resultFile.println(numberOfKnownModels + "\t" + me.getPrecision() + "\t" +
              // me.getRecall());
              String s = me.getPrecision() + "\t" + me.getRecall() + "\t" + elapsedTimeSec;
              if (resultsArray[numberOfKnownModels + 2].length() > 0)
                resultsArray[numberOfKnownModels + 2].append(" \t ");
              resultsArray[numberOfKnownModels + 2].append(s);

              //							resultFile.println(me.getPrecision() + "\t" + me.getRecall() + "\t" +
              // elapsedTimeSec);
            }
          }

        String outName =
            !iterativeEvaluation
                ? outputPath
                    + semanticModels.get(newSourceIndex).getName()
                    + Params.GRAPHVIS_OUT_DETAILS_FILE_EXT
                : outputPath
                    + semanticModels.get(newSourceIndex).getName()
                    + ".knownModels="
                    + numberOfKnownModels
                    + Params.GRAPHVIS_OUT_DETAILS_FILE_EXT;

        //	if (!iterativeEvaluation) {
        GraphVizUtil.exportSemanticModelsToGraphviz(
            models,
            newSource.getName(),
            outName,
            GraphVizLabelType.LocalId,
            GraphVizLabelType.LocalUri,
            false,
            false);
        //				}

        numberOfKnownModels++;
      }

      //	resultFile.println("=======================================================");
    }
    for (StringBuffer s : resultsArray) resultFile.println(s.toString());

    resultFile.close();
  }
  @Override
  public UpdateContainer doIt(Workspace workspace) throws CommandException {
    ModelingConfiguration modelingConfiguration =
        ModelingConfigurationRegistry.getInstance()
            .getModelingConfiguration(
                WorkspaceKarmaHomeRegistry.getInstance().getKarmaHome(workspace.getId()));
    TripleStoreUtil utilObj = new TripleStoreUtil();
    boolean showModelsWithoutMatching = modelingConfiguration.isShowModelsWithoutMatching();
    try {
      HashMap<String, List<String>> metadata =
          utilObj.getMappingsWithMetadata(TripleStoreUrl, context);
      RepFactory factory = workspace.getFactory();
      List<String> model_Names = metadata.get("model_names");
      List<String> model_Urls = metadata.get("model_urls");
      List<String> model_Times = metadata.get("model_publishtimes");
      List<String> model_Contexts = metadata.get("model_contexts");
      List<String> model_inputColumns = metadata.get("model_inputcolumns");
      final List<JSONObject> list = new ArrayList<>();
      Set<String> worksheetcolumns = new HashSet<>();
      if (worksheetId != null && !worksheetId.trim().isEmpty()) {
        HTable htable = factory.getWorksheet(worksheetId).getHeaders();
        getHNodesForWorksheet(htable, worksheetcolumns, factory);
      }
      Iterator<String> nameitr = model_Names.iterator();
      Iterator<String> urlitr = model_Urls.iterator();
      Iterator<String> timeitr = model_Times.iterator();
      Iterator<String> contextitr = model_Contexts.iterator();
      Iterator<String> inputitr = model_inputColumns.iterator();
      while (nameitr.hasNext()
          && urlitr.hasNext()
          && timeitr.hasNext()
          && contextitr.hasNext()
          && inputitr.hasNext()) {
        JSONObject obj = new JSONObject();
        Set<String> inputs = new HashSet<>();
        obj.put("name", nameitr.next());
        obj.put("url", urlitr.next());
        obj.put("publishTime", timeitr.next());
        obj.put("context", contextitr.next());
        String columns = inputitr.next();
        if (columns != null && !columns.isEmpty()) {
          JSONArray array = new JSONArray(columns);
          for (int i = 0; i < array.length(); i++) inputs.add(array.get(i).toString());
        } else if (showModelsWithoutMatching) {
          list.add(obj);
        }
        if (worksheetId != null && !worksheetId.isEmpty()) {
          inputs.retainAll(worksheetcolumns);
          obj.put("inputColumns", inputs.size());
        } else obj.put("inputColumns", 0);
        if (!inputs.isEmpty() || (worksheetId == null || worksheetId.trim().isEmpty()))
          list.add(obj);
      }

      Collections.sort(
          list,
          new Comparator<JSONObject>() {

            @Override
            public int compare(JSONObject a, JSONObject b) {
              return b.getInt("inputColumns") - a.getInt("inputColumns");
            }
          });

      return new UpdateContainer(
          new AbstractUpdate() {
            @Override
            public void generateJson(String prefix, PrintWriter pw, VWorkspace vWorkspace) {
              try {
                JSONArray array = new JSONArray();
                for (JSONObject obj : list) {
                  array.put(obj);
                }
                pw.print(array.toString());
              } catch (Exception e) {
                logger.error("Error generating JSON!", e);
              }
            }
          });
    } catch (Exception e) {
      return new UpdateContainer(
          new ErrorUpdate("Unable to get mappings with metadata: " + e.getMessage()));
    }
  }