Example usage for weka.classifiers.evaluation Prediction weight

List of usage examples for weka.classifiers.evaluation Prediction weight

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Introduction

In this page you can find the example usage for weka.classifiers.evaluation Prediction weight.

Prototype

double weight();

Source Link

Document

Gets the weight assigned to this prediction.

Usage

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

License:Open Source License

/**
 * Executes the flow item./* w  ww  .j a  va 2 s .  c o 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<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 ava  2s.c  o 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;
}