コード例 #1
0
ファイル: KMeans.java プロジェクト: huamichaelchen/h2o-3
    private TwoDimTable createScoringHistoryTable(KMeansModel.KMeansOutput output) {
      List<String> colHeaders = new ArrayList<>();
      List<String> colTypes = new ArrayList<>();
      List<String> colFormat = new ArrayList<>();
      colHeaders.add("Timestamp");
      colTypes.add("string");
      colFormat.add("%s");
      colHeaders.add("Duration");
      colTypes.add("string");
      colFormat.add("%s");
      colHeaders.add("Iteration");
      colTypes.add("long");
      colFormat.add("%d");
      colHeaders.add("Avg. Change of Std. Centroids");
      colTypes.add("double");
      colFormat.add("%.5f");
      colHeaders.add("Within Cluster Sum Of Squares");
      colTypes.add("double");
      colFormat.add("%.5f");

      final int rows = output._avg_centroids_chg.length;
      TwoDimTable table =
          new TwoDimTable(
              "Scoring History",
              null,
              new String[rows],
              colHeaders.toArray(new String[0]),
              colTypes.toArray(new String[0]),
              colFormat.toArray(new String[0]),
              "");
      int row = 0;
      for (int i = 0; i < rows; i++) {
        int col = 0;
        assert (row < table.getRowDim());
        assert (col < table.getColDim());
        DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
        table.set(row, col++, fmt.print(output._training_time_ms[i]));
        table.set(row, col++, PrettyPrint.msecs(output._training_time_ms[i] - _start_time, true));
        table.set(row, col++, i);
        table.set(row, col++, output._avg_centroids_chg[i]);
        table.set(row, col++, output._history_withinss[i]);
        row++;
      }
      return table;
    }
コード例 #2
0
ファイル: SharedTree.java プロジェクト: herberteuler/h2o-3
  private TwoDimTable createScoringHistoryTable(SharedTreeModel.SharedTreeOutput _output) {
    List<String> colHeaders = new ArrayList<>();
    List<String> colTypes = new ArrayList<>();
    List<String> colFormat = new ArrayList<>();
    colHeaders.add("Timestamp");
    colTypes.add("string");
    colFormat.add("%s");
    colHeaders.add("Duration");
    colTypes.add("string");
    colFormat.add("%s");
    colHeaders.add("Number of Trees");
    colTypes.add("long");
    colFormat.add("%d");
    colHeaders.add("Training MSE");
    colTypes.add("double");
    colFormat.add("%.5f");
    if (_output.isClassifier()) {
      colHeaders.add("Training LogLoss");
      colTypes.add("double");
      colFormat.add("%.5f");
    }
    if (_output.getModelCategory() == ModelCategory.Binomial) {
      colHeaders.add("Training AUC");
      colTypes.add("double");
      colFormat.add("%.5f");
    }
    if (_output.getModelCategory() == ModelCategory.Binomial
        || _output.getModelCategory() == ModelCategory.Multinomial) {
      colHeaders.add("Training Classification Error");
      colTypes.add("double");
      colFormat.add("%.5f");
    }

    if (valid() != null) {
      colHeaders.add("Validation MSE");
      colTypes.add("double");
      colFormat.add("%.5f");
      if (_output.isClassifier()) {
        colHeaders.add("Validation LogLoss");
        colTypes.add("double");
        colFormat.add("%.5f");
      }
      if (_output.getModelCategory() == ModelCategory.Binomial) {
        colHeaders.add("Validation AUC");
        colTypes.add("double");
        colFormat.add("%.5f");
      }
      if (_output.isClassifier()) {
        colHeaders.add("Validation Classification Error");
        colTypes.add("double");
        colFormat.add("%.5f");
      }
    }

    int rows = 0;
    for (int i = 1; i < _output._scored_train.length; i++) {
      if (!Double.isNaN(_output._scored_train[i]._mse)) ++rows;
    }
    TwoDimTable table =
        new TwoDimTable(
            "Scoring History",
            null,
            new String[rows],
            colHeaders.toArray(new String[0]),
            colTypes.toArray(new String[0]),
            colFormat.toArray(new String[0]),
            "");
    int row = 0;
    for (int i = 1; i < _output._scored_train.length; i++) {
      if (Double.isNaN(_output._scored_train[i]._mse)) continue;
      int col = 0;
      assert (row < table.getRowDim());
      assert (col < table.getColDim());
      DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
      table.set(row, col++, fmt.print(_output._training_time_ms[i]));
      table.set(row, col++, PrettyPrint.msecs(_output._training_time_ms[i] - _start_time, true));
      table.set(row, col++, i);
      ScoreKeeper st = _output._scored_train[i];
      table.set(row, col++, st._mse);
      if (_output.isClassifier()) table.set(row, col++, st._logloss);
      if (_output.getModelCategory() == ModelCategory.Binomial) table.set(row, col++, st._AUC);
      if (_output.isClassifier()) table.set(row, col++, st._classError);

      if (_valid != null) {
        st = _output._scored_valid[i];
        table.set(row, col++, st._mse);
        if (_output.isClassifier()) table.set(row, col++, st._logloss);
        if (_output.getModelCategory() == ModelCategory.Binomial) table.set(row, col++, st._AUC);
        if (_output.isClassifier()) table.set(row, col++, st._classError);
      }
      row++;
    }
    return table;
  }
コード例 #3
0
ファイル: GridSearch.java プロジェクト: hihihippp/h2o
    @Override
    public boolean toHTML(StringBuilder sb) {
      if (jobs != null) {
        DocGen.HTML.arrayHead(sb);
        sb.append("<tr class='warning'>");
        ArrayList<Argument> args = jobs[0].arguments();
        // Filter some keys to simplify UI
        args = (ArrayList<Argument>) args.clone();
        filter(
            args,
            "destination_key",
            "source",
            "cols",
            "ignored_cols_by_name",
            "response",
            "classification",
            "validation");
        for (int i = 0; i < args.size(); i++)
          sb.append("<td><b>").append(args.get(i)._name).append("</b></td>");
        sb.append("<td><b>").append("run time").append("</b></td>");
        String perf = jobs[0].speedDescription();
        if (perf != null) sb.append("<td><b>").append(perf).append("</b></td>");
        sb.append("<td><b>").append("model key").append("</b></td>");
        sb.append("<td><b>").append("prediction error").append("</b></td>");
        sb.append("<td><b>").append("F1 score").append("</b></td>");
        sb.append("</tr>");

        ArrayList<JobInfo> infos = new ArrayList<JobInfo>();
        for (Job job : jobs) {
          JobInfo info = new JobInfo();
          info._job = job;
          Object value = UKV.get(job.destination_key);
          info._model = value instanceof Model ? (Model) value : null;
          if (info._model != null) info._cm = info._model.cm();
          if (info._cm != null) info._error = info._cm.err();
          infos.add(info);
        }
        Collections.sort(
            infos,
            new Comparator<JobInfo>() {
              @Override
              public int compare(JobInfo a, JobInfo b) {
                return Double.compare(a._error, b._error);
              }
            });

        for (JobInfo info : infos) {
          sb.append("<tr>");
          for (Argument a : args) {
            try {
              Object value = a._field.get(info._job);
              String s;
              if (value instanceof int[]) s = Utils.sampleToString((int[]) value, 20);
              else s = "" + value;
              sb.append("<td>").append(s).append("</td>");
            } catch (Exception e) {
              throw new RuntimeException(e);
            }
          }
          String runTime = "Pending", speed = "";
          if (info._job.start_time != 0) {
            runTime = PrettyPrint.msecs(info._job.runTimeMs(), true);
            speed = perf != null ? PrettyPrint.msecs(info._job.speedValue(), true) : "";
          }
          sb.append("<td>").append(runTime).append("</td>");
          if (perf != null) sb.append("<td>").append(speed).append("</td>");

          String link = info._job.destination_key.toString();
          if (info._job.start_time != 0 && DKV.get(info._job.destination_key) != null) {
            if (info._model instanceof GBMModel)
              link = GBMModelView.link(link, info._job.destination_key);
            else if (info._model instanceof NeuralNetModel)
              link = NeuralNetProgress.link(info._job.self(), info._job.destination_key, link);
            if (info._model instanceof KMeans2Model)
              link = KMeans2ModelView.link(link, info._job.destination_key);
            else link = Inspect.link(link, info._job.destination_key);
          }
          sb.append("<td>").append(link).append("</td>");

          String pct = "", f1 = "";
          if (info._cm != null) {
            pct = String.format("%.2f", 100 * info._error) + "%";
            if (info._cm._arr.length == 2)
              f1 = String.format("%.2f", info._cm.precisionAndRecall());
          }
          sb.append("<td><b>").append(pct).append("</b></td>");
          sb.append("<td><b>").append(f1).append("</b></td>");
          sb.append("</tr>");
        }
        DocGen.HTML.arrayTail(sb);
      }
      return true;
    }