Example usage for weka.classifiers Evaluation predictions

List of usage examples for weka.classifiers Evaluation predictions

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Introduction

In this page you can find the example usage for weka.classifiers Evaluation predictions.

Prototype

public ArrayList<Prediction> predictions()

Source Link

Document

Returns the predictions that have been collected.

Usage

From source file:adams.flow.sink.WekaMarginCurve.java

License:Open Source License

/**
 * Plots the token (the panel and dialog have already been created at
 * this stage).// www  .  j  a v  a  2 s . c o  m
 *
 * @param token   the token to display
 */
@Override
protected void display(Token token) {
    weka.classifiers.evaluation.MarginCurve curve;
    Evaluation eval;
    PlotData2D plot;
    boolean[] connectPoints;
    int cp;
    Instances data;

    try {
        if (token.getPayload() instanceof WekaEvaluationContainer)
            eval = (Evaluation) ((WekaEvaluationContainer) token.getPayload())
                    .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
        else
            eval = (Evaluation) token.getPayload();
        if (eval.predictions() == null) {
            getLogger().severe("No predictions available from Evaluation object!");
            return;
        }
        curve = new weka.classifiers.evaluation.MarginCurve();
        data = curve.getCurve(eval.predictions());
        plot = new PlotData2D(data);
        plot.m_displayAllPoints = true;
        connectPoints = new boolean[data.numInstances()];
        for (cp = 1; cp < connectPoints.length; cp++)
            connectPoints[cp] = true;
        plot.setConnectPoints(connectPoints);
        m_VisualizePanel.addPlot(plot);
    } catch (Exception e) {
        handleException("Failed to display token: " + token, e);
    }
}

From source file:adams.flow.sink.WekaMarginCurve.java

License:Open Source License

/**
 * Creates a new panel for the token.//from   ww  w . j ava  2  s . c om
 *
 * @param token   the token to display in a new panel, can be null
 * @return      the generated panel
 */
public AbstractDisplayPanel createDisplayPanel(Token token) {
    AbstractDisplayPanel result;
    String name;

    if (token != null)
        name = "Margin curve (" + getEvaluation(token).getHeader().relationName() + ")";
    else
        name = "Margin curve";

    result = new AbstractComponentDisplayPanel(name) {
        private static final long serialVersionUID = -3513994354297811163L;
        protected VisualizePanel m_VisualizePanel;

        @Override
        protected void initGUI() {
            super.initGUI();
            setLayout(new BorderLayout());
            m_VisualizePanel = new VisualizePanel();
            add(m_VisualizePanel, BorderLayout.CENTER);
        }

        @Override
        public void display(Token token) {
            try {
                Evaluation eval = getEvaluation(token);
                weka.classifiers.evaluation.MarginCurve curve = new weka.classifiers.evaluation.MarginCurve();
                Instances data = curve.getCurve(eval.predictions());
                PlotData2D plot = new PlotData2D(data);
                plot.m_displayAllPoints = true;
                boolean[] connectPoints = new boolean[data.numInstances()];
                for (int cp = 1; cp < connectPoints.length; cp++)
                    connectPoints[cp] = true;
                plot.setConnectPoints(connectPoints);
                m_VisualizePanel.addPlot(plot);
            } catch (Exception e) {
                getLogger().log(Level.SEVERE, "Failed to display token: " + token, e);
            }
        }

        @Override
        public JComponent supplyComponent() {
            return m_VisualizePanel;
        }

        @Override
        public void clearPanel() {
            m_VisualizePanel.removeAllPlots();
        }

        public void cleanUp() {
            m_VisualizePanel.removeAllPlots();
        }
    };

    if (token != null)
        result.display(token);

    return result;
}

From source file:adams.flow.sink.WekaThresholdCurve.java

License:Open Source License

/**
 * Plots the token (the panel and dialog have already been created at
 * this stage).//from   w  w w . ja  va 2  s .  c o m
 *
 * @param token   the token to display
 */
@Override
protected void display(Token token) {
    ThresholdCurve curve;
    Evaluation eval;
    PlotData2D plot;
    boolean[] connectPoints;
    int cp;
    Instances data;
    int[] indices;

    try {
        if (token.getPayload() instanceof WekaEvaluationContainer)
            eval = (Evaluation) ((WekaEvaluationContainer) token.getPayload())
                    .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
        else
            eval = (Evaluation) token.getPayload();
        if (eval.predictions() == null) {
            getLogger().severe("No predictions available from Evaluation object!");
            return;
        }
        m_ClassLabelRange.setData(eval.getHeader().classAttribute());
        indices = m_ClassLabelRange.getIntIndices();
        for (int index : indices) {
            curve = new ThresholdCurve();
            data = curve.getCurve(eval.predictions(), index);
            plot = new PlotData2D(data);
            plot.setPlotName(eval.getHeader().classAttribute().value(index));
            plot.m_displayAllPoints = true;
            connectPoints = new boolean[data.numInstances()];
            for (cp = 1; cp < connectPoints.length; cp++)
                connectPoints[cp] = true;
            plot.setConnectPoints(connectPoints);
            m_VisualizePanel.addPlot(plot);
            if (data.attribute(m_AttributeX.toDisplay()) != null)
                m_VisualizePanel.setXIndex(data.attribute(m_AttributeX.toDisplay()).index());
            if (data.attribute(m_AttributeY.toDisplay()) != null)
                m_VisualizePanel.setYIndex(data.attribute(m_AttributeY.toDisplay()).index());
        }
    } catch (Exception e) {
        handleException("Failed to display token: " + token, e);
    }
}

From source file:adams.flow.sink.WekaThresholdCurve.java

License:Open Source License

/**
 * Creates a new panel for the token./*from  w  w w.j a va  2 s  .c  o m*/
 *
 * @param token   the token to display in a new panel, can be null
 * @return      the generated panel
 */
public AbstractDisplayPanel createDisplayPanel(Token token) {
    AbstractDisplayPanel result;
    String name;

    if (token != null)
        name = "Threshold curve (" + getEvaluation(token).getHeader().relationName() + ")";
    else
        name = "Threshold curve";

    result = new AbstractComponentDisplayPanel(name) {
        private static final long serialVersionUID = -7362768698548152899L;
        protected ThresholdVisualizePanel m_VisualizePanel;

        @Override
        protected void initGUI() {
            super.initGUI();
            setLayout(new BorderLayout());
            m_VisualizePanel = new ThresholdVisualizePanel();
            add(m_VisualizePanel, BorderLayout.CENTER);
        }

        @Override
        public void display(Token token) {
            try {
                Evaluation eval = getEvaluation(token);
                m_ClassLabelRange.setMax(eval.getHeader().classAttribute().numValues());
                int[] indices = m_ClassLabelRange.getIntIndices();
                for (int index : indices) {
                    ThresholdCurve curve = new ThresholdCurve();
                    Instances data = curve.getCurve(eval.predictions(), index);
                    PlotData2D plot = new PlotData2D(data);
                    plot.setPlotName(eval.getHeader().classAttribute().value(index));
                    plot.m_displayAllPoints = true;
                    boolean[] connectPoints = new boolean[data.numInstances()];
                    for (int cp = 1; cp < connectPoints.length; cp++)
                        connectPoints[cp] = true;
                    plot.setConnectPoints(connectPoints);
                    m_VisualizePanel.addPlot(plot);
                    if (data.attribute(m_AttributeX.toDisplay()) != null)
                        m_VisualizePanel.setXIndex(data.attribute(m_AttributeX.toDisplay()).index());
                    if (data.attribute(m_AttributeY.toDisplay()) != null)
                        m_VisualizePanel.setYIndex(data.attribute(m_AttributeY.toDisplay()).index());
                }
            } catch (Exception e) {
                getLogger().log(Level.SEVERE, "Failed to display token: " + token, e);
            }
        }

        @Override
        public JComponent supplyComponent() {
            return m_VisualizePanel;
        }

        @Override
        public void clearPanel() {
            m_VisualizePanel.removeAllPlots();
        }

        public void cleanUp() {
            m_VisualizePanel.removeAllPlots();
        }
    };

    if (token != null)
        result.display(token);

    return result;
}

From source file:adams.flow.transformer.WekaAccumulatedError.java

License:Open Source License

/**
 * Executes the flow item.// w  w  w  .  j  a v  a  2s .  c om
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    ArrayList predictions;
    int i;
    Evaluation eval;

    result = null;

    if (m_InputToken.getPayload() instanceof WekaEvaluationContainer)
        eval = (Evaluation) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
    else
        eval = (Evaluation) m_InputToken.getPayload();
    predictions = eval.predictions();
    if (predictions != null) {
        m_NumPredictions = predictions.size();
        for (i = 0; i < predictions.size(); i++)
            m_Predictions.add(new SortablePrediction((Prediction) predictions.get(i)));
        Collections.sort(m_Predictions);
    } else {
        getLogger().severe("No predictions available from Evaluation object!");
    }

    return result;
}

From source file:adams.flow.transformer.WekaBootstrapping.java

License:Open Source License

/**
 * Executes the flow item./*from   ww  w.  j  a  va2s .  c o  m*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    SpreadSheet sheet;
    Row row;
    Evaluation evalAll;
    Evaluation eval;
    WekaEvaluationContainer cont;
    TIntList indices;
    Random random;
    int i;
    int iteration;
    int size;
    List<Prediction> preds;
    Instances header;
    Instances data;
    ArrayList<Attribute> atts;
    Instance inst;
    boolean numeric;
    int classIndex;
    Double[] errors;
    Double[] errorsRev;
    Percentile<Double> perc;
    Percentile<Double> percRev;
    TIntList subset;

    result = null;

    if (m_InputToken.getPayload() instanceof Evaluation) {
        evalAll = (Evaluation) m_InputToken.getPayload();
    } else {
        cont = (WekaEvaluationContainer) m_InputToken.getPayload();
        evalAll = (Evaluation) cont.getValue(WekaEvaluationContainer.VALUE_EVALUATION);
    }

    if ((evalAll.predictions() == null) || (evalAll.predictions().size() == 0))
        result = "No predictions available!";

    if (result == null) {
        // init spreadsheet
        sheet = new DefaultSpreadSheet();
        row = sheet.getHeaderRow();
        row.addCell("S").setContentAsString("Subsample");
        for (EvaluationStatistic s : m_StatisticValues)
            row.addCell(s.toString()).setContentAsString(s.toString());
        for (i = 0; i < m_Percentiles.length; i++) {
            switch (m_ErrorCalculation) {
            case ACTUAL_MINUS_PREDICTED:
                row.addCell("perc-AmP-" + i).setContentAsString("Percentile-AmP-" + m_Percentiles[i]);
                break;
            case PREDICTED_MINUS_ACTUAL:
                row.addCell("perc-PmA-" + i).setContentAsString("Percentile-PmA-" + m_Percentiles[i]);
                break;
            case ABSOLUTE:
                row.addCell("perc-Abs-" + i).setContentAsString("Percentile-Abs-" + m_Percentiles[i]);
                break;
            case BOTH:
                row.addCell("perc-AmP-" + i).setContentAsString("Percentile-AmP-" + m_Percentiles[i]);
                row.addCell("perc-PmA-" + i).setContentAsString("Percentile-PmA-" + m_Percentiles[i]);
                break;
            default:
                throw new IllegalStateException("Unhandled error calculation: " + m_ErrorCalculation);
            }
        }

        // set up bootstrapping
        preds = evalAll.predictions();
        random = new Random(m_Seed);
        indices = new TIntArrayList();
        size = (int) Math.round(preds.size() * m_Percentage);
        header = evalAll.getHeader();
        numeric = header.classAttribute().isNumeric();
        m_ClassIndex.setData(header.classAttribute());
        if (numeric)
            classIndex = -1;
        else
            classIndex = m_ClassIndex.getIntIndex();
        for (i = 0; i < preds.size(); i++)
            indices.add(i);

        // create fake evalutions
        subset = new TIntArrayList();
        for (iteration = 0; iteration < m_NumSubSamples; iteration++) {
            if (isStopped()) {
                sheet = null;
                break;
            }

            // determine
            subset.clear();
            if (m_WithReplacement) {
                for (i = 0; i < size; i++)
                    subset.add(indices.get(random.nextInt(preds.size())));
            } else {
                indices.shuffle(random);
                for (i = 0; i < size; i++)
                    subset.add(indices.get(i));
            }

            // create dataset from predictions
            errors = new Double[size];
            errorsRev = new Double[size];
            atts = new ArrayList<>();
            atts.add(header.classAttribute().copy("Actual"));
            data = new Instances(header.relationName() + "-" + (iteration + 1), atts, size);
            data.setClassIndex(0);
            for (i = 0; i < subset.size(); i++) {
                inst = new DenseInstance(preds.get(subset.get(i)).weight(),
                        new double[] { preds.get(subset.get(i)).actual() });
                data.add(inst);
                switch (m_ErrorCalculation) {
                case ACTUAL_MINUS_PREDICTED:
                    errors[i] = preds.get(subset.get(i)).actual() - preds.get(subset.get(i)).predicted();
                    break;
                case PREDICTED_MINUS_ACTUAL:
                    errorsRev[i] = preds.get(subset.get(i)).predicted() - preds.get(subset.get(i)).actual();
                    break;
                case ABSOLUTE:
                    errors[i] = Math
                            .abs(preds.get(subset.get(i)).actual() - preds.get(subset.get(i)).predicted());
                    break;
                case BOTH:
                    errors[i] = preds.get(subset.get(i)).actual() - preds.get(subset.get(i)).predicted();
                    errorsRev[i] = preds.get(subset.get(i)).predicted() - preds.get(subset.get(i)).actual();
                    break;
                default:
                    throw new IllegalStateException("Unhandled error calculation: " + m_ErrorCalculation);
                }
            }

            // perform "fake" evaluation
            try {
                eval = new Evaluation(data);
                for (i = 0; i < subset.size(); i++) {
                    if (numeric)
                        eval.evaluateModelOnceAndRecordPrediction(
                                new double[] { preds.get(subset.get(i)).predicted() }, data.instance(i));
                    else
                        eval.evaluateModelOnceAndRecordPrediction(
                                ((NominalPrediction) preds.get(subset.get(i))).distribution().clone(),
                                data.instance(i));
                }
            } catch (Exception e) {
                result = handleException(
                        "Failed to create 'fake' Evaluation object (iteration: " + (iteration + 1) + ")!", e);
                break;
            }

            // add row
            row = sheet.addRow();
            row.addCell("S").setContent(iteration + 1);
            for (EvaluationStatistic s : m_StatisticValues) {
                try {
                    row.addCell(s.toString()).setContent(EvaluationHelper.getValue(eval, s, classIndex));
                } catch (Exception e) {
                    getLogger().log(Level.SEVERE,
                            "Failed to calculate statistic in iteration #" + (iteration + 1) + ": " + s, e);
                    row.addCell(s.toString()).setMissing();
                }
            }
            for (i = 0; i < m_Percentiles.length; i++) {
                perc = new Percentile<>();
                perc.addAll(errors);
                percRev = new Percentile<>();
                percRev.addAll(errorsRev);
                switch (m_ErrorCalculation) {
                case ACTUAL_MINUS_PREDICTED:
                    row.addCell("perc-AmP-" + i).setContent(perc.getPercentile(m_Percentiles[i].doubleValue()));
                    break;
                case PREDICTED_MINUS_ACTUAL:
                    row.addCell("perc-PmA-" + i)
                            .setContent(percRev.getPercentile(m_Percentiles[i].doubleValue()));
                    break;
                case ABSOLUTE:
                    row.addCell("perc-Abs-" + i).setContent(perc.getPercentile(m_Percentiles[i].doubleValue()));
                    break;
                case BOTH:
                    row.addCell("perc-AmP-" + i).setContent(perc.getPercentile(m_Percentiles[i].doubleValue()));
                    row.addCell("perc-PmA-" + i)
                            .setContent(percRev.getPercentile(m_Percentiles[i].doubleValue()));
                    break;
                default:
                    throw new IllegalStateException("Unhandled error calculation: " + m_ErrorCalculation);
                }
            }
        }

        if ((result == null) && (sheet != null))
            m_OutputToken = new Token(sheet);
    }

    return result;
}

From source file:adams.flow.transformer.WekaEvaluationInfo.java

License:Open Source License

/**
 * Executes the flow item./*w  ww  .  ja  v  a2s.  c  om*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    Evaluation eval;

    if (m_InputToken.getPayload() instanceof Evaluation)
        eval = (Evaluation) m_InputToken.getPayload();
    else
        eval = (Evaluation) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_EVALUATION);

    switch (m_Type) {
    case RELATION_NAME:
        m_OutputToken = new Token(eval.getHeader().relationName());
        break;
    case CLASS_ATTRIBUTE_NAME:
        m_OutputToken = new Token(eval.getHeader().classAttribute().name());
        break;
    case HEADER:
        m_OutputToken = new Token(eval.getHeader());
        break;
    case PREDICTIONS_RECORDED:
        m_OutputToken = new Token(eval.predictions() != null);
        break;
    case NUM_PREDICTIONS:
        if (eval.predictions() == null)
            m_OutputToken = new Token(-1);
        else
            m_OutputToken = new Token(eval.predictions().size());
        break;
    default:
        throw new IllegalStateException("Unhandled info type: " + m_Type);
    }

    return null;
}

From source file:adams.flow.transformer.WekaPredictionsToInstances.java

License:Open Source License

/**
 * Executes the flow item./* ww w.java  2s  .c  om*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    Evaluation eval;
    int i;
    int n;
    int indexErr;
    int indexProb;
    int indexDist;
    int indexWeight;
    boolean nominal;
    Instances header;
    ArrayList<Attribute> atts;
    ArrayList<String> values;
    ArrayList<Prediction> predictions;
    Prediction pred;
    double[] vals;
    Instances data;
    Instances testData;
    int[] indices;

    result = null;

    if (m_InputToken.getPayload() instanceof WekaEvaluationContainer) {
        eval = (Evaluation) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
        indices = (int[]) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_ORIGINALINDICES);
        testData = (Instances) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_TESTDATA);
    } else {
        eval = (Evaluation) m_InputToken.getPayload();
        indices = null;
        testData = null;
    }
    header = eval.getHeader();
    nominal = header.classAttribute().isNominal();
    predictions = eval.predictions();

    if (predictions != null) {
        // create header
        atts = new ArrayList<>();
        // actual
        if (nominal && m_AddLabelIndex) {
            values = new ArrayList<>();
            for (i = 0; i < header.classAttribute().numValues(); i++)
                values.add((i + 1) + ":" + header.classAttribute().value(i));
            atts.add(new Attribute(m_MeasuresPrefix + "Actual", values));
        } else {
            atts.add(header.classAttribute().copy(m_MeasuresPrefix + "Actual"));
        }
        // predicted
        if (nominal && m_AddLabelIndex) {
            values = new ArrayList<>();
            for (i = 0; i < header.classAttribute().numValues(); i++)
                values.add((i + 1) + ":" + header.classAttribute().value(i));
            atts.add(new Attribute(m_MeasuresPrefix + "Predicted", values));
        } else {
            atts.add(header.classAttribute().copy(m_MeasuresPrefix + "Predicted"));
        }
        // error
        indexErr = -1;
        if (m_ShowError) {
            indexErr = atts.size();
            if (nominal) {
                values = new ArrayList<>();
                values.add("n");
                values.add("y");
                atts.add(new Attribute(m_MeasuresPrefix + "Error", values));
            } else {
                atts.add(new Attribute(m_MeasuresPrefix + "Error"));
            }
        }
        // probability
        indexProb = -1;
        if (m_ShowProbability && nominal) {
            indexProb = atts.size();
            atts.add(new Attribute(m_MeasuresPrefix + "Probability"));
        }
        // distribution
        indexDist = -1;
        if (m_ShowDistribution && nominal) {
            indexDist = atts.size();
            for (n = 0; n < header.classAttribute().numValues(); n++)
                atts.add(new Attribute(
                        m_MeasuresPrefix + "Distribution (" + header.classAttribute().value(n) + ")"));
        }
        // weight
        indexWeight = -1;
        if (m_ShowWeight) {
            indexWeight = atts.size();
            atts.add(new Attribute(m_MeasuresPrefix + "Weight"));
        }

        data = new Instances("Predictions", atts, predictions.size());
        data.setClassIndex(1); // predicted

        // add data
        if ((indices != null) && m_UseOriginalIndices)
            predictions = CrossValidationHelper.alignPredictions(predictions, indices);
        for (i = 0; i < predictions.size(); i++) {
            pred = predictions.get(i);
            vals = new double[data.numAttributes()];
            // actual
            vals[0] = pred.actual();
            // predicted
            vals[1] = pred.predicted();
            // error
            if (m_ShowError) {
                if (nominal) {
                    vals[indexErr] = ((pred.actual() != pred.predicted()) ? 1.0 : 0.0);
                } else {
                    if (m_UseAbsoluteError)
                        vals[indexErr] = Math.abs(pred.actual() - pred.predicted());
                    else
                        vals[indexErr] = pred.actual() - pred.predicted();
                }
            }
            // probability
            if (m_ShowProbability && nominal) {
                vals[indexProb] = StatUtils.max(((NominalPrediction) pred).distribution());
            }
            // distribution
            if (m_ShowDistribution && nominal) {
                for (n = 0; n < header.classAttribute().numValues(); n++)
                    vals[indexDist + n] = ((NominalPrediction) pred).distribution()[n];
            }
            // weight
            if (m_ShowWeight) {
                vals[indexWeight] = pred.weight();
            }
            // add row
            data.add(new DenseInstance(1.0, vals));
        }

        // add test data?
        if ((testData != null) && !m_TestAttributes.isEmpty()) {
            testData = filterTestData(testData);
            if (testData != null)
                data = Instances.mergeInstances(data, testData);
        }

        // generate output token
        m_OutputToken = new Token(data);
    } else {
        getLogger().severe("No predictions available from Evaluation object!");
    }

    return result;
}

From source file:adams.flow.transformer.WekaPredictionsToSpreadSheet.java

License:Open Source License

/**
 * Executes the flow item.//from   w w  w. j  a va2s.co  m
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    Evaluation eval;
    int i;
    int n;
    int indexErr;
    int indexProb;
    int indexDist;
    int indexWeight;
    boolean nominal;
    Instances header;
    ArrayList<Prediction> predictions;
    Prediction pred;
    SpreadSheet data;
    Instances testData;
    InstancesView testView;
    Row row;
    int[] indices;

    result = null;

    if (m_InputToken.getPayload() instanceof WekaEvaluationContainer) {
        eval = (Evaluation) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
        indices = (int[]) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_ORIGINALINDICES);
        testData = (Instances) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_TESTDATA);
    } else {
        eval = (Evaluation) m_InputToken.getPayload();
        indices = null;
        testData = null;
    }
    header = eval.getHeader();
    nominal = header.classAttribute().isNominal();
    predictions = eval.predictions();

    if (predictions != null) {
        data = new DefaultSpreadSheet();
        data.setName("Predictions");

        // create header
        row = data.getHeaderRow();
        row.addCell("A").setContent(m_MeasuresPrefix + "Actual");
        row.addCell("P").setContent(m_MeasuresPrefix + "Predicted");
        indexErr = -1;
        if (m_ShowError) {
            indexErr = row.getCellCount();
            row.addCell("E").setContent(m_MeasuresPrefix + "Error");
        }
        // probability
        indexProb = -1;
        if (m_ShowProbability && nominal) {
            indexProb = row.getCellCount();
            row.addCell("Pr").setContent(m_MeasuresPrefix + "Probability");
        }
        // distribution
        indexDist = -1;
        if (m_ShowDistribution && nominal) {
            indexDist = row.getCellCount();
            for (n = 0; n < header.classAttribute().numValues(); n++)
                row.addCell("D" + n).setContent(
                        m_MeasuresPrefix + "Distribution (" + header.classAttribute().value(n) + ")");
        }
        // weight
        indexWeight = -1;
        if (m_ShowWeight) {
            indexWeight = row.getCellCount();
            row.addCell("W").setContent(m_MeasuresPrefix + "Weight");
        }

        // add data
        if ((indices != null) && m_UseOriginalIndices)
            predictions = CrossValidationHelper.alignPredictions(predictions, indices);
        for (i = 0; i < predictions.size(); i++) {
            pred = predictions.get(i);
            row = data.addRow();
            // actual
            if (Double.isNaN(pred.actual()))
                row.addCell(0).setMissing();
            else if (nominal)
                row.addCell(0).setContentAsString(header.classAttribute().value((int) pred.actual()));
            else
                row.addCell(0).setContent(pred.actual());
            // predicted
            if (Double.isNaN(pred.predicted()))
                row.addCell(1).setMissing();
            else if (nominal)
                row.addCell(1).setContentAsString(header.classAttribute().value((int) pred.predicted()));
            else
                row.addCell(1).setContent(pred.predicted());
            // error
            if (m_ShowError) {
                if (nominal) {
                    row.addCell(indexErr).setContent((pred.actual() != pred.predicted() ? "true" : "false"));
                } else {
                    if (m_UseAbsoluteError)
                        row.addCell(indexErr).setContent(Math.abs(pred.actual() - pred.predicted()));
                    else
                        row.addCell(indexErr).setContent(pred.actual() - pred.predicted());
                }
            }
            // probability
            if (m_ShowProbability && nominal) {
                row.addCell(indexProb).setContent(StatUtils.max(((NominalPrediction) pred).distribution()));
            }
            // distribution
            if (m_ShowDistribution && nominal) {
                for (n = 0; n < header.classAttribute().numValues(); n++)
                    row.addCell(indexDist + n).setContent(((NominalPrediction) pred).distribution()[n]);
            }
            // weight
            if (m_ShowWeight) {
                row.addCell(indexWeight).setContent(pred.weight());
            }
        }

        // add test data?
        if ((testData != null) && !m_TestAttributes.isEmpty()) {
            testData = filterTestData(testData);
            if (testData != null) {
                testView = new InstancesView(testData);
                data.mergeWith(testView);
            }
        }

        // generate output token
        m_OutputToken = new Token(data);
    } else {
        getLogger().severe("No predictions available from Evaluation object!");
    }

    return result;
}

From source file:adams.gui.visualization.debug.inspectionhandler.WekaEvaluation.java

License:Open Source License

/**
 * Returns further inspection values.//w  w  w . j a  v  a 2  s  . co  m
 *
 * @param obj      the object to further inspect
 * @return      the named inspected values
 */
@Override
public Hashtable<String, Object> inspect(Object obj) {
    Hashtable<String, Object> result;
    Evaluation eval;
    WekaEvaluationValues values;
    boolean numeric;
    boolean nominal;
    List<EvaluationStatistic> stats;
    String msg;
    Token token;

    result = new Hashtable<>();

    eval = (Evaluation) obj;

    result.put("header", eval.getHeader());
    result.put("predictions", eval.predictions());

    nominal = eval.getHeader().classAttribute().isNominal();
    numeric = eval.getHeader().classAttribute().isNumeric();
    stats = new ArrayList<>();
    for (EvaluationStatistic stat : EvaluationStatistic.values()) {
        if (nominal && stat.isOnlyNominal())
            stats.add(stat);
        else if (numeric && stat.isOnlyNumeric())
            stats.add(stat);
        else if (!stat.isOnlyNumeric() && !stat.isOnlyNominal())
            stats.add(stat);
    }
    values = new WekaEvaluationValues();
    values.setStatisticValues(stats.toArray(new EvaluationStatistic[stats.size()]));
    values.input(new Token(eval));
    msg = values.execute();
    if (msg == null) {
        token = values.output();
        if (token != null)
            result.put("statistics", token.getPayload());
    } else {
        System.err.println(getClass().getName() + ": Failed to extract statistics:\n" + msg);
    }

    return result;
}