Examples with CSVLoader org.tribuo.data.csv.CSVLoader used on opensource projects

Example 1 with CSVLoader

use of org.tribuo.data.csv.CSVLoader in project tribuo by oracle.

the class CSVLoaderWithMultiOutputsTest method loadsMultiRegressor.

@Test
public void loadsMultiRegressor() throws IOException {
    Path path = Resources.copyResourceToTmp("/org/tribuo/tests/csv/multioutput-regression.csv");
    CSVLoader<Regressor> loader = new CSVLoader<>(new RegressionFactory());
    String[] responseNames = new String[] { "R1", "R2" };
    MutableDataset<Regressor> data = loader.load(path, new HashSet<>(Arrays.asList(responseNames)));
    assertEquals(5, data.size());
    Example<Regressor> x0 = data.getExample(0);
    assertArrayEquals(responseNames, x0.getOutput().getNames());
    assertArrayEquals(new double[] { 0.1, 0.2 }, x0.getOutput().getValues());
    Example<Regressor> x1 = data.getExample(1);
    assertArrayEquals(responseNames, x1.getOutput().getNames());
    assertArrayEquals(new double[] { 0.0, 0.0 }, x1.getOutput().getValues());
}

Example 2 with CSVLoader

use of org.tribuo.data.csv.CSVLoader in project tribuo by oracle.

the class CSVSaverWithMultiOutputsTest method loaderCanReconstructSavedMultiLabel.

@Test
public void loaderCanReconstructSavedMultiLabel() throws IOException {
    Path path = Resources.copyResourceToTmp("/org/tribuo/tests/csv/multilabel.csv");
    Set<String> responses = new HashSet<>(Arrays.asList("R1", "R2"));
    // 
    // Load the csv
    CSVLoader<MultiLabel> loader = new CSVLoader<>(new MultiLabelFactory());
    MutableDataset<MultiLabel> before = loader.load(path, responses);
    // 
    // Save the dataset
    File tmpFile = File.createTempFile("tribuo-csv-test", "csv");
    tmpFile.deleteOnExit();
    Path tmp = tmpFile.toPath();
    new CSVSaver().save(tmp, before, responses);
    // 
    // Reload and check that before & after are equivalent.
    MutableDataset<MultiLabel> after = loader.load(tmp, responses);
    // 
    // TODO: better check for dataset equivalence?
    assertEquals(before.getData(), after.getData());
    assertEquals(before.getOutputIDInfo().size(), after.getOutputIDInfo().size());
    assertEquals(before.getFeatureIDMap().size(), after.getFeatureIDMap().size());
}

Example 3 with CSVLoader

use of org.tribuo.data.csv.CSVLoader in project tribuo by oracle.

the class CSVSaverWithMultiOutputsTest method savesMultipleRegression.

@Test
public void savesMultipleRegression() throws IOException {
    String[] vars = new String[] { "dim1", "dim2" };
    Set<String> responseNames = new HashSet<>(Arrays.asList("dim1", "dim2"));
    RegressionFactory factory = new RegressionFactory();
    MutableDataset<Regressor> before = new MutableDataset<>(null, factory);
    ArrayExample<Regressor> e = new ArrayExample<>(new Regressor(vars, new double[] { 0.1, 0.0 }));
    e.add(new Feature("A", 1.0));
    e.add(new Feature("B", 0.0));
    e.add(new Feature("C", 0.0));
    before.add(e);
    ArrayExample<Regressor> b = new ArrayExample<>(new Regressor(vars, new double[] { 0.0, 0.0 }));
    b.add(new Feature("A", 1.0));
    b.add(new Feature("B", 0.0));
    b.add(new Feature("C", 0.1));
    before.add(b);
    CSVSaver saver = new CSVSaver();
    File tmpFile = File.createTempFile("tribuo-csv-test", "csv");
    tmpFile.deleteOnExit();
    Path tmp = tmpFile.toPath();
    saver.save(tmp, before, responseNames);
    // TODO use this to compare literal saver outputs
    // ByteArrayOutputStream baos = new ByteArrayOutputStream();
    // saver.save(baos, before, responseNames);
    // baos.flush();
    // System.out.println(new String(baos.toByteArray()));
    CSVLoader<Regressor> loader = new CSVLoader<>(factory);
    MutableDataset<Regressor> after = loader.load(tmp, responseNames);
    assertEquals(before.getData(), after.getData());
    assertEquals(before.getOutputIDInfo().size(), after.getOutputIDInfo().size());
    assertEquals(before.getFeatureIDMap().size(), after.getFeatureIDMap().size());
}

Example 4 with CSVLoader

use of org.tribuo.data.csv.CSVLoader in project tribuo by oracle.

the class CSVSaverWithMultiOutputsTest method savesMultiLabel.

@Test
public void savesMultiLabel() throws IOException {
    Set<String> responseNames = new HashSet<>(Arrays.asList("MONKEY", "PUZZLE", "TREE"));
    MultiLabelFactory factory = new MultiLabelFactory();
    MutableDataset<MultiLabel> before = new MutableDataset<>(null, factory);
    ArrayExample<MultiLabel> e = new ArrayExample<>(factory.generateOutput("MONKEY"));
    e.add(new Feature("A-MONKEY", 1.0));
    e.add(new Feature("B-PUZZLE", 0.0));
    e.add(new Feature("C-TREE", 0.0));
    before.add(e);
    ArrayExample<MultiLabel> b = new ArrayExample<>(factory.generateOutput("MONKEY,TREE"));
    b.add(new Feature("A-MONKEY", 1.0));
    b.add(new Feature("C-TREE", 1.0));
    CSVSaver saver = new CSVSaver();
    File tmpFile = File.createTempFile("tribuo-csv-test", "csv");
    tmpFile.deleteOnExit();
    Path tmp = tmpFile.toPath();
    saver.save(tmp, before, responseNames);
    // TODO use this to compare literal saver outputs
    // ByteArrayOutputStream baos = new ByteArrayOutputStream();
    // saver.save(baos, before, responseNames);
    // baos.flush();
    // System.out.println(new String(baos.toByteArray()));
    CSVLoader<MultiLabel> loader = new CSVLoader<>(factory);
    MutableDataset<MultiLabel> after = loader.load(tmp, responseNames);
    assertEquals(before.getData(), after.getData());
    assertEquals(before.getOutputIDInfo().size(), after.getOutputIDInfo().size());
    assertEquals(before.getFeatureIDMap().size(), after.getFeatureIDMap().size());
}

Example 5 with CSVLoader

use of org.tribuo.data.csv.CSVLoader in project tribuo by oracle.

the class DataOptions method load.

/**
 * Loads the training and testing data from {@link #trainingPath} and {@link #testingPath}
 * according to the other parameters specified in this class.
 * @param outputFactory The output factory to use to process the inputs.
 * @param <T> The dataset output type.
 * @return A pair containing the training and testing datasets. The training dataset is element 'A' and the
 * testing dataset is element 'B'.
 * @throws IOException If the paths could not be loaded.
 */
public <T extends Output<T>> Pair<Dataset<T>, Dataset<T>> load(OutputFactory<T> outputFactory) throws IOException {
    logger.info(String.format("Loading data from %s", trainingPath));
    Dataset<T> train;
    Dataset<T> test;
    char separator;
    switch(inputFormat) {
        case SERIALIZED:
            // 
            // Load Tribuo serialised datasets.
            logger.info("Deserialising dataset from " + trainingPath);
            try (ObjectInputStream ois = new ObjectInputStream(new BufferedInputStream(new FileInputStream(trainingPath.toFile())));
                ObjectInputStream oits = new ObjectInputStream(new BufferedInputStream(new FileInputStream(testingPath.toFile())))) {
                @SuppressWarnings("unchecked") Dataset<T> tmp = (Dataset<T>) ois.readObject();
                train = tmp;
                if (minCount > 0) {
                    logger.info("Found " + train.getFeatureIDMap().size() + " features");
                    logger.info("Removing features that occur fewer than " + minCount + " times.");
                    train = new MinimumCardinalityDataset<>(train, minCount);
                }
                logger.info(String.format("Loaded %d training examples for %s", train.size(), train.getOutputs().toString()));
                logger.info("Found " + train.getFeatureIDMap().size() + " features, and " + train.getOutputInfo().size() + " response dimensions");
                @SuppressWarnings("unchecked") Dataset<T> deserTest = (Dataset<T>) oits.readObject();
                test = new ImmutableDataset<>(deserTest, deserTest.getSourceProvenance(), deserTest.getOutputFactory(), train.getFeatureIDMap(), train.getOutputIDInfo(), true);
            } catch (ClassNotFoundException e) {
                throw new IllegalArgumentException("Unknown class in serialised files", e);
            }
            break;
        case LIBSVM:
            // 
            // Load the libsvm text-based data format.
            LibSVMDataSource<T> trainSVMSource = new LibSVMDataSource<>(trainingPath, outputFactory);
            train = new MutableDataset<>(trainSVMSource);
            boolean zeroIndexed = trainSVMSource.isZeroIndexed();
            int maxFeatureID = trainSVMSource.getMaxFeatureID();
            if (minCount > 0) {
                logger.info("Removing features that occur fewer than " + minCount + " times.");
                train = new MinimumCardinalityDataset<>(train, minCount);
            }
            logger.info(String.format("Loaded %d training examples for %s", train.size(), train.getOutputs().toString()));
            logger.info("Found " + train.getFeatureIDMap().size() + " features, and " + train.getOutputInfo().size() + " response dimensions");
            test = new ImmutableDataset<>(new LibSVMDataSource<>(testingPath, outputFactory, zeroIndexed, maxFeatureID), train.getFeatureIDMap(), train.getOutputIDInfo(), false);
            break;
        case TEXT:
            // 
            // Using a simple Java break iterator to generate ngram features.
            TextFeatureExtractor<T> extractor;
            if (hashDim > 0) {
                extractor = new TextFeatureExtractorImpl<>(new TokenPipeline(new BreakIteratorTokenizer(Locale.US), ngram, termCounting, hashDim));
            } else {
                extractor = new TextFeatureExtractorImpl<>(new TokenPipeline(new BreakIteratorTokenizer(Locale.US), ngram, termCounting));
            }
            TextDataSource<T> trainSource = new SimpleTextDataSource<>(trainingPath, outputFactory, extractor);
            train = new MutableDataset<>(trainSource);
            if (minCount > 0) {
                logger.info("Removing features that occur fewer than " + minCount + " times.");
                train = new MinimumCardinalityDataset<>(train, minCount);
            }
            logger.info(String.format("Loaded %d training examples for %s", train.size(), train.getOutputs().toString()));
            logger.info("Found " + train.getFeatureIDMap().size() + " features, and " + train.getOutputInfo().size() + " response dimensions");
            TextDataSource<T> testSource = new SimpleTextDataSource<>(testingPath, outputFactory, extractor);
            test = new ImmutableDataset<>(testSource, train.getFeatureIDMap(), train.getOutputIDInfo(), false);
            break;
        case CSV:
            // Load the data using the simple CSV loader
            if (csvResponseName == null) {
                throw new IllegalArgumentException("Please supply a response column name");
            }
            separator = delimiter.value;
            CSVLoader<T> loader = new CSVLoader<>(separator, outputFactory);
            train = new MutableDataset<>(loader.loadDataSource(trainingPath, csvResponseName));
            logger.info(String.format("Loaded %d training examples for %s", train.size(), train.getOutputs().toString()));
            logger.info("Found " + train.getFeatureIDMap().size() + " features, and " + train.getOutputInfo().size() + " response dimensions");
            test = new MutableDataset<>(loader.loadDataSource(testingPath, csvResponseName));
            break;
        case COLUMNAR:
            if (rowProcessor == null) {
                throw new IllegalArgumentException("Please supply a RowProcessor");
            }
            OutputFactory<?> rowOutputFactory = rowProcessor.getResponseProcessor().getOutputFactory();
            if (!rowOutputFactory.equals(outputFactory)) {
                throw new IllegalArgumentException("The RowProcessor doesn't use the same kind of OutputFactory as the one supplied. RowProcessor has " + rowOutputFactory.getClass().getSimpleName() + ", supplied " + outputFactory.getClass().getName());
            }
            // checked by the if statement above
            @SuppressWarnings("unchecked") RowProcessor<T> typedRowProcessor = (RowProcessor<T>) rowProcessor;
            separator = delimiter.value;
            train = new MutableDataset<>(new CSVDataSource<>(trainingPath, typedRowProcessor, true, separator, csvQuoteChar));
            logger.info(String.format("Loaded %d training examples for %s", train.size(), train.getOutputs().toString()));
            logger.info("Found " + train.getFeatureIDMap().size() + " features, and " + train.getOutputInfo().size() + " response dimensions");
            test = new MutableDataset<>(new CSVDataSource<>(testingPath, typedRowProcessor, true, separator, csvQuoteChar));
            break;
        default:
            throw new IllegalArgumentException("Unsupported input format " + inputFormat);
    }
    logger.info(String.format("Loaded %d testing examples", test.size()));
    if (scaleFeatures) {
        logger.info("Fitting feature scaling");
        TransformationMap map = new TransformationMap(Collections.singletonList(new LinearScalingTransformation()));
        TransformerMap transformers = train.createTransformers(map, scaleIncZeros);
        logger.info("Applying scaling to training dataset");
        train = transformers.transformDataset(train);
        logger.info("Applying scaling to testing dataset");
        test = transformers.transformDataset(test);
    }
    return new Pair<>(train, test);
}