Examples with WekaException weka.core.WekaException used on opensource projects

Example 1 with WekaException

use of weka.core.WekaException in project labkit-ui by juglab.

the class TrainableSegmentationSegmenter method train.

@Override
public void train(List<Pair<ImgPlus<?>, Labeling>> trainingData) {
    try {
        initFeatureSettings(trainingData);
        List<String> classes = collectLabels(trainingData.stream().map(Pair::getB).collect(Collectors.toList()));
        sc.fiji.labkit.pixel_classification.classification.Segmenter segmenter = new sc.fiji.labkit.pixel_classification.classification.Segmenter(context, classes, featureSettings, new FastRandomForest());
        segmenter.setUseGpu(useGpu);
        Training training = segmenter.training();
        for (Pair<ImgPlus<?>, Labeling> pair : trainingData) trainStack(training, classes, pair.getB(), pair.getA(), segmenter.features());
        training.train();
        this.segmenter = segmenter;
    } catch (RuntimeException e) {
        Throwable cause = e.getCause();
        if (cause instanceof WekaException && cause.getMessage().contains("Not enough training instances"))
            throw new CancellationException("The training requires some labeled regions.");
        throw e;
    }
}

Example 2 with WekaException

use of weka.core.WekaException in project umple by umple.

the class ExplicitTestsetResultProducer method doRun.

/**
 * Gets the results for a specified run number. Different run numbers
 * correspond to different randomizations of the data. Results produced should
 * be sent to the current ResultListener
 *
 * @param run the run number to get results for.
 * @throws Exception if a problem occurs while getting the results
 */
@Override
public void doRun(int run) throws Exception {
    if (getRawOutput()) {
        if (m_ZipDest == null) {
            m_ZipDest = new OutputZipper(m_OutputFile);
        }
    }
    if (m_Instances == null) {
        throw new Exception("No Instances set");
    }
    // Add in some fields to the key like run number, dataset name
    Object[] seKey = m_SplitEvaluator.getKey();
    Object[] key = new Object[seKey.length + 2];
    key[0] = Utils.backQuoteChars(m_Instances.relationName());
    key[1] = "" + run;
    System.arraycopy(seKey, 0, key, 2, seKey.length);
    if (m_ResultListener.isResultRequired(this, key)) {
        // training set
        Instances train = new Instances(m_Instances);
        if (m_randomize) {
            Random rand = new Random(run);
            train.randomize(rand);
        }
        if (m_env == null) {
            m_env = new Environment();
        }
        m_env.addVariable("RUN_NUMBER", "" + run);
        // test set
        String filename = createFilename(train);
        File file = new File(filename);
        if (!file.exists()) {
            throw new WekaException("Test set '" + filename + "' not found!");
        }
        Instances test = DataSource.read(filename);
        // can we set the class attribute safely?
        if (train.numAttributes() == test.numAttributes()) {
            test.setClassIndex(train.classIndex());
        } else {
            throw new WekaException("Train and test set (= " + filename + ") " + "differ in number of attributes: " + train.numAttributes() + " != " + test.numAttributes());
        }
        // test headers
        if (!train.equalHeaders(test)) {
            throw new WekaException("Train and test set (= " + filename + ") " + "are not compatible:\n" + train.equalHeadersMsg(test));
        }
        try {
            Object[] seResults = m_SplitEvaluator.getResult(train, test);
            Object[] results = new Object[seResults.length + 1];
            results[0] = getTimestamp();
            System.arraycopy(seResults, 0, results, 1, seResults.length);
            if (m_debugOutput) {
                String resultName = ("" + run + "." + Utils.backQuoteChars(train.relationName()) + "." + m_SplitEvaluator.toString()).replace(' ', '_');
                resultName = Utils.removeSubstring(resultName, "weka.classifiers.");
                resultName = Utils.removeSubstring(resultName, "weka.filters.");
                resultName = Utils.removeSubstring(resultName, "weka.attributeSelection.");
                m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
            }
            m_ResultListener.acceptResult(this, key, results);
        } catch (Exception e) {
            // Save the train and test datasets for debugging purposes?
            throw e;
        }
    }
}

Example 3 with WekaException

use of weka.core.WekaException in project umple by umple.

the class Script method runScript.

/**
 * Runs the specified script. All options that weren't "consumed" (like "-s"
 * for the script filename), will be used as commandline arguments for the
 * actual script.
 *
 * @param script the script object to use
 * @param args the commandline arguments
 * @throws Exception if execution fails
 */
public static void runScript(Script script, String[] args) throws Exception {
    String tmpStr;
    File scriptFile;
    Vector<String> options;
    int i;
    if (Utils.getFlag('h', args) || Utils.getFlag("help", args)) {
        System.out.println(makeOptionString(script));
    } else {
        // process options
        tmpStr = Utils.getOption('s', args);
        if (tmpStr.length() == 0) {
            throw new WekaException("No script supplied!");
        } else {
            scriptFile = new File(tmpStr);
        }
        script.setOptions(args);
        // remove empty elements from array
        options = new Vector<String>();
        for (i = 0; i < args.length; i++) {
            if (args[i].length() > 0) {
                options.add(args[i]);
            }
        }
        // run script
        script.run(scriptFile, options.toArray(new String[options.size()]));
    }
}

Example 4 with WekaException

use of weka.core.WekaException in project umple by umple.

the class AbstractOutput method printFooter.

/**
 * Prints the footer to the buffer. This will also store the generated output
 * in a file if an output file was specified.
 *
 * @throws Exception if check fails
 */
public void printFooter() throws Exception {
    String error;
    BufferedWriter writer;
    if ((error = checkBasic()) != null) {
        throw new WekaException(error);
    }
    doPrintFooter();
    // write output to file
    if (!m_OutputFile.isDirectory()) {
        try {
            writer = new BufferedWriter(new FileWriter(m_OutputFile));
            writer.write(m_FileBuffer.toString());
            writer.newLine();
            writer.flush();
            writer.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Example 5 with WekaException

use of weka.core.WekaException in project ambit-mirror by ideaconsult.

the class FilteredWekaModelBuilder method process.

public ModelQueryResults process(Algorithm algorithm) throws AmbitException {
    List<Filter> filters = new ArrayList<Filter>();
    Instances instances = trainingData;
    if ((instances == null) || (instances.numInstances() == 0) || (instances.numAttributes() == 0))
        throw new ResourceException(Status.CLIENT_ERROR_BAD_REQUEST, "Empty dataset!");
    Object weka = null;
    try {
        Class clazz = this.getClass().getClassLoader().loadClass(algorithm.getContent().toString());
        weka = clazz.newInstance();
    } catch (Exception x) {
        throw new ResourceException(Status.CLIENT_ERROR_BAD_REQUEST, x.getMessage(), x);
    }
    if (targetURI != null)
        for (String t : targetURI) for (int i = 0; i < instances.numAttributes(); i++) if (instances.attribute(i).name().equals(t)) {
            instances.setClassIndex(i);
            break;
        }
    fclusterer = null;
    fclassifier = null;
    pca = null;
    if (weka instanceof Clusterer) {
        fclusterer = new FilteredClusterer();
        fclusterer.setClusterer((Clusterer) weka);
    } else if (weka instanceof Classifier) {
        fclassifier = new FilteredClassifier();
        fclassifier.setClassifier((Classifier) weka);
        if (targetURI == null)
            throw new ResourceException(Status.CLIENT_ERROR_BAD_REQUEST, "No target variable! " + OpenTox.params.target);
        if (instances.classIndex() < 0)
            throw new ResourceException(Status.CLIENT_ERROR_BAD_REQUEST, "No target variable! " + OpenTox.params.target);
        if (weka instanceof IBk) {
            String[] options = new String[3];
            options[0] = "-K";
            options[1] = "-20";
            options[2] = "-X";
            try {
                ((IBk) weka).setOptions(options);
            } catch (Exception x) {
            }
        }
    } else if (weka instanceof PrincipalComponents) {
        pca = (PrincipalComponents) weka;
    } else
        throw new AmbitException(String.format("Unknown algorithm %s", algorithm.toString()));
    String[] prm = algorithm.getParametersAsArray();
    if (prm != null)
        try {
            if (fclassifier != null)
                fclassifier.getClassifier().setOptions(prm);
            else if (pca != null)
                pca.setOptions(prm);
            else if (fclusterer != null) {
                fclusterer.getClusterer().getClass().getMethod("setOptions", new Class[] {}).invoke(fclusterer.getClusterer(), prm);
            }
        } catch (Exception x) {
            Context.getCurrentLogger().warning("Error setting algorithm parameters, assuming defaults" + x.getMessage());
        }
    try {
        // remove firstCompoundID attribute
        String[] options = new String[2];
        options[0] = "-R";
        options[1] = "1";
        Remove remove = new Remove();
        remove.setOptions(options);
        filters.add(remove);
    } catch (Exception x) {
        throw new AmbitException(x);
    }
    try {
        // remove missing values
        if (!hasCapability(Capability.MISSING_VALUES)) {
            ReplaceMissingValues missing = new ReplaceMissingValues();
            // can't make it working with RemoveWithValues...
            String[] options = new String[1];
            options[0] = "-M";
            missing.setOptions(options);
            filters.add(missing);
        }
    } catch (Exception x) {
        throw new AmbitException(x);
    }
    if (instances.classIndex() >= 0)
        try {
            // num/nom support
            if (instances.attribute(instances.classIndex()).isNominal()) {
                if (!hasCapability(Capability.NOMINAL_CLASS)) {
                    if (hasCapability(Capability.BINARY_CLASS)) {
                        // nominal 2 binary
                        NominalToBinary nom2bin = new NominalToBinary();
                        String[] options = new String[2];
                        options[0] = "-R";
                        options[1] = Integer.toString(instances.classIndex());
                        nom2bin.setOptions(options);
                        filters.add(nom2bin);
                    }
                }
            } else if (instances.attribute(instances.classIndex()).isNumeric()) {
                if (!hasCapability(Capability.NUMERIC_CLASS)) {
                    if (hasCapability(Capability.NOMINAL_CLASS)) {
                        // numeric to nominal, i.e. Discretize
                        Discretize num2nom = new Discretize();
                        String[] options = new String[2];
                        options[0] = "-R";
                        options[1] = Integer.toString(instances.classIndex());
                        num2nom.setOptions(options);
                        filters.add(num2nom);
                    }
                }
            // else all is well
            } else if (instances.attribute(instances.classIndex()).isString()) {
                if (hasCapability(Capability.NOMINAL_CLASS)) {
                    StringToNominal str2nom = new StringToNominal();
                    String[] options = new String[2];
                    options[0] = "-R";
                    options[1] = Integer.toString(instances.classIndex());
                    str2nom.setOptions(options);
                    filters.add(str2nom);
                }
            }
            if (!hasCapability(Capability.MISSING_CLASS_VALUES)) {
                RemoveWithValues missing = new RemoveWithValues();
                String[] options = new String[3];
                options[0] = "-M";
                options[1] = "-C";
                options[2] = Integer.toString(instances.classIndex());
                missing.setOptions(options);
                filters.add(missing);
            }
            if (fclassifier == null) {
                // clusterer, ignore the class attr
                try {
                    // remove firstCompoundID attribute
                    String[] options = new String[2];
                    options[0] = "-R";
                    options[1] = Integer.toString(instances.classIndex());
                    Remove remove = new Remove();
                    remove.setOptions(options);
                    filters.add(remove);
                } catch (Exception x) {
                    throw new AmbitException(x);
                }
            }
        } catch (Exception x) {
            throw new AmbitException(x);
        }
    try {
        // all besides the class (if set!)
        filters.add(new Standardize());
    } catch (Exception x) {
        throw new AmbitException(x);
    }
    // now set the filters
    MultiFilter filter = new MultiFilter();
    filter.setFilters(filters.toArray(new Filter[filters.size()]));
    Instances newInstances = instances;
    if (fclassifier != null)
        fclassifier.setFilter(filter);
    else if (fclusterer != null)
        fclusterer.setFilter(filter);
    else {
        try {
            filter.setInputFormat(instances);
            newInstances = Filter.useFilter(instances, filter);
        } catch (Exception x) {
            throw new AmbitException(x);
        }
    }
    SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyMMddhhmmss");
    Date timestamp = new Date(System.currentTimeMillis());
    String name = String.format("%s.%s.%s", simpleDateFormat.format(new Date(System.currentTimeMillis())), UUID.randomUUID().toString(), weka.getClass().getName());
    ModelQueryResults m = new ModelQueryResults();
    m.setParameters(parameters);
    m.setId(null);
    m.setContentMediaType(AlgorithmFormat.WEKA.getMediaType());
    m.setName(name);
    m.setAlgorithm(alg_reporter.getURI(algorithm));
    AlgorithmURIReporter r = new AlgorithmURIReporter();
    LiteratureEntry entry = new LiteratureEntry(name, algorithm == null ? weka.getClass().getName() : r.getURI(applicationRootReference.toString(), algorithm));
    LiteratureEntry prediction = new LiteratureEntry(m.getName(), model_reporter.getURI(applicationRootReference.toString(), m));
    prediction.setType(_type.Model);
    Template predictors = null;
    Template dependent = null;
    PredictedVarsTemplate predicted = null;
    if (fclusterer != null) {
        try {
            fclusterer.buildClusterer(newInstances);
        } catch (Exception x) {
            throw new AmbitException(x);
        }
        predicted = new PredictedVarsTemplate(name + "#Predicted");
        Property property = new Property("Cluster", prediction);
        property.setNominal(true);
        predicted.add(property);
        dependent = new Template("Empty");
        predictors = new Template(name + "#Independent");
        for (int i = 0; i < newInstances.numAttributes(); i++) {
            property = createPropertyFromReference(new Reference(newInstances.attribute(i).name()), entry, referer);
            property.setOrder(i + 1);
            predictors.add(property);
        }
    } else if (fclassifier != null) {
        try {
            System.out.println(fclassifier.getClassifier().getCapabilities());
            fclassifier.getCapabilities().testWithFail(newInstances);
        } catch (Exception x) {
            throw new AmbitException(x);
        }
        try {
            // if (classifier instanceof LinearRegression) //don't do feature selection!
            // classifier.setOptions(new String[] {"-S","1"});
            StringBuilder evaluationString = new StringBuilder();
            EvaluationStats<String> stats = new EvaluationStats<String>(EVType.crossvalidation, null);
            Evaluation eval = new Evaluation(newInstances);
            if (newInstances.numInstances() > 20) {
                eval.crossValidateModel(fclassifier, newInstances, 10, new Random(1));
                evaluationString.append("Crossvalidation 10 folds\n");
            } else {
                eval.crossValidateModel(fclassifier, newInstances, 2, new Random(1));
                evaluationString.append("Crossvalidation 2 folds\n");
            }
            try {
                evaluationString.append(eval.toSummaryString());
                evaluationString.append("\n");
            } catch (Exception x) {
            }
            try {
                evaluationString.append(eval.toClassDetailsString());
                evaluationString.append("\n");
                evaluationString.append(eval.toMatrixString());
                evaluationString.append("\n");
            } catch (Exception x) {
            }
            try {
                evaluationString.append(eval.weightedAreaUnderROC());
            } catch (Exception x) {
            }
            try {
                stats.setMAE(eval.meanAbsoluteError());
            } catch (Exception x) {
            }
            try {
                stats.setRMSE(eval.rootMeanSquaredError());
            } catch (Exception x) {
            }
            try {
                stats.setPctCorrect(eval.pctCorrect());
                stats.setPctInCorrect(eval.pctIncorrect());
            } catch (Exception x) {
            }
            stats.setContent(evaluationString.toString());
            m.addEvaluation(stats);
            stats = new EvaluationStats<String>(EVType.evaluation_training, null);
            evaluationString = new StringBuilder();
            fclassifier.buildClassifier(newInstances);
            eval = new Evaluation(newInstances);
            eval.evaluateModel(fclassifier, newInstances);
            try {
                evaluationString.append("\nTraining dataset statistics\n");
                evaluationString.append(eval.toSummaryString());
                evaluationString.append("\n");
            } catch (Exception x) {
            }
            try {
                evaluationString.append(eval.toMatrixString());
                evaluationString.append("\n");
            } catch (Exception x) {
            }
            try {
                stats.setMAE(eval.meanAbsoluteError());
            } catch (Exception x) {
            }
            try {
                stats.setRMSE(eval.rootMeanSquaredError());
            } catch (Exception x) {
            }
            try {
                stats.setPctCorrect(eval.pctCorrect());
                stats.setPctInCorrect(eval.pctIncorrect());
            } catch (Exception x) {
            }
            stats.setContent(evaluationString.toString());
            m.addEvaluation(stats);
        } catch (WekaException x) {
            throw new AmbitException(x);
        } catch (Exception x) {
            throw new AmbitException(x);
        }
        ;
        dependent = new Template(name + "#Dependent");
        Property property = createPropertyFromReference(new Reference(newInstances.attribute(newInstances.classIndex()).name()), entry, referer);
        dependent.add(property);
        predicted = new PredictedVarsTemplate(name + "#Predicted");
        Property predictedProperty = new Property(property.getName(), prediction);
        predictedProperty.setLabel(property.getLabel());
        predictedProperty.setUnits(property.getUnits());
        predictedProperty.setClazz(property.getClazz());
        predictedProperty.setNominal(property.isNominal());
        predicted.add(predictedProperty);
        predictedProperty.setEnabled(true);
        if (supportsDistribution(fclassifier)) {
            Property confidenceProperty = new Property(String.format("%s Confidence", property.getName()), prediction);
            confidenceProperty.setLabel(Property.opentox_ConfidenceFeature);
            confidenceProperty.setUnits("");
            confidenceProperty.setClazz(Number.class);
            confidenceProperty.setEnabled(true);
            PropertyAnnotation<Property> a = new PropertyAnnotation<Property>();
            a.setType(OT.OTClass.ModelConfidenceFeature.name());
            a.setPredicate(OT.OTProperty.confidenceOf.name());
            a.setObject(predictedProperty);
            PropertyAnnotations aa = new PropertyAnnotations();
            aa.add(a);
            confidenceProperty.setAnnotations(aa);
            predicted.add(confidenceProperty);
        }
        predictors = new Template(name + "#Independent");
        for (int i = 0; i < newInstances.numAttributes(); i++) {
            if ("CompoundURI".equals(newInstances.attribute(i).name()))
                continue;
            if (newInstances.classIndex() == i)
                continue;
            property = createPropertyFromReference(new Reference(newInstances.attribute(i).name()), entry, referer);
            property.setOrder(i + 1);
            predictors.add(property);
        }
    } else if (pca != null) {
        try {
            pca.setVarianceCovered(1.0);
            pca.buildEvaluator(newInstances);
        } catch (Exception x) {
            throw new AmbitException(x);
        }
        Property property;
        dependent = new Template("Empty");
        predictors = new Template(name + "#Independent");
        for (int i = 0; i < newInstances.numAttributes(); i++) {
            if ("CompoundURI".equals(newInstances.attribute(i).name()))
                continue;
            if (newInstances.classIndex() == i)
                continue;
            property = createPropertyFromReference(new Reference(newInstances.attribute(i).name()), entry, referer);
            property.setOrder(i + 1);
            predictors.add(property);
        }
        predicted = new PredictedVarsTemplate(name + "#Predicted");
        for (int i = 0; i < newInstances.numAttributes(); i++) {
            if (newInstances.classIndex() == i)
                continue;
            property = createPropertyFromReference(new Reference(String.format("PCA_%d", i + 1)), entry, referer);
            property.setClazz(Number.class);
            property.setOrder(i + 1);
            predicted.add(property);
        }
    }
    m.setPredictors(predictors);
    m.setDependent(dependent);
    m.setPredicted(predicted);
    try {
        serializeModel(fclusterer == null ? fclassifier == null ? pca : fclassifier : fclusterer, newInstances, m);
    } catch (Exception x) {
        throw new AmbitException(x);
    }
    return m;
}