smile.data.Attribute Java Examples
The following examples show how to use
smile.data.Attribute.
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Example #1
Source File: SmileRandomForest.java From kogito-runtimes with Apache License 2.0 | 6 votes |
public SmileRandomForest(Map<String, AttributeType> inputFeatures, String outputFeatureName, AttributeType outputFeatureType, double confidenceThreshold, int numberTrees) { super(inputFeatures, outputFeatureName, outputFeatureType, confidenceThreshold); this.numberTrees = numberTrees; smileAttributes = new HashMap<>(); for (Entry<String, AttributeType> inputFeature : inputFeatures.entrySet()) { final String name = inputFeature.getKey(); final AttributeType type = inputFeature.getValue(); smileAttributes.put(name, createAttribute(name, type)); attributeNames.add(name); } numAttributes = smileAttributes.size(); outcomeAttribute = createAttribute(outputFeatureName, outputFeatureType); outcomeAttributeType = outputFeatureType; dataset = new AttributeDataset("dataset", smileAttributes.values().toArray(new Attribute[numAttributes]), outcomeAttribute); }
Example #2
Source File: SmileRandomForest.java From kogito-runtimes with Apache License 2.0 | 5 votes |
protected Attribute createAttribute(String name, AttributeType type) { if (type == AttributeType.NOMINAL || type == AttributeType.BOOLEAN) { return new NominalAttribute(name); } else if (type == AttributeType.NUMERIC) { return new NumericAttribute(name); } else { return new StringAttribute(name); } }
Example #3
Source File: LinkClassifierTrainer.java From ache with Apache License 2.0 | 5 votes |
/** * Converts the input instances into an AttributeDataset object that can be used to train a * SMILE classifier. * * @param attributes * @param instances * @param wrapper * @param dataset * @throws IOException */ private AttributeDataset createDataset(List<Sampler<LinkNeighborhood>> instances, String[] features, List<String> classValues, LinkNeighborhoodWrapper wrapper) { List<Attribute> attributes = new ArrayList<>(); for(String featureName : features) { NumericAttribute attribute = new NumericAttribute(featureName); attributes.add(attribute); } Attribute[] attributesArray = (Attribute[]) attributes.toArray(new Attribute[attributes.size()]); String[] classValuesArray = (String[]) classValues.toArray(new String[classValues.size()]); String description = "If link leads to relevant page or not."; Attribute response = new NominalAttribute("y", description, classValuesArray); AttributeDataset dataset = new AttributeDataset("link_classifier", attributesArray, response); for (int level = 0; level < instances.size(); level++) { Sampler<LinkNeighborhood> levelSamples = instances.get(level); for (LinkNeighborhood ln : levelSamples.getSamples()) { Instance instance; try { instance = wrapper.extractToInstance(ln, features); } catch (MalformedURLException e) { logger.warn("Failed to process intance: "+ln.getLink().toString(), e); continue; } double[] values = instance.getValues(); // the instance's feature vector int y = level; // the class we're trying to predict dataset.add(values, y); } } return dataset; }
Example #4
Source File: DecisionTreeTest.java From incubator-hivemall with Apache License 2.0 | 4 votes |
private static void runTracePredict(String datasetUrl, int responseIndex, int numLeafs) throws IOException, ParseException { URL url = new URL(datasetUrl); InputStream is = new BufferedInputStream(url.openStream()); ArffParser arffParser = new ArffParser(); arffParser.setResponseIndex(responseIndex); AttributeDataset ds = arffParser.parse(is); final Attribute[] attrs = ds.attributes(); final Attribute targetAttr = ds.response(); double[][] x = ds.toArray(new double[ds.size()][]); int[] y = ds.toArray(new int[ds.size()]); Random rnd = new Random(43L); int numTrain = (int) (x.length * 0.7); int[] index = ArrayUtils.shuffle(MathUtils.permutation(x.length), rnd); int[] cvTrain = Arrays.copyOf(index, numTrain); int[] cvTest = Arrays.copyOfRange(index, numTrain, index.length); double[][] trainx = Math.slice(x, cvTrain); int[] trainy = Math.slice(y, cvTrain); double[][] testx = Math.slice(x, cvTest); DecisionTree tree = new DecisionTree(SmileExtUtils.convertAttributeTypes(attrs), matrix(trainx, false), trainy, numLeafs, RandomNumberGeneratorFactory.createPRNG(43L)); final LinkedHashMap<String, Double> map = new LinkedHashMap<>(); final StringBuilder buf = new StringBuilder(); for (int i = 0; i < testx.length; i++) { final DenseVector test = new DenseVector(testx[i]); tree.predict(test, new PredictionHandler() { @Override public void visitBranch(Operator op, int splitFeatureIndex, double splitFeature, double splitValue) { buf.append(attrs[splitFeatureIndex].name); buf.append(" [" + splitFeature + "] "); buf.append(op); buf.append(' '); buf.append(splitValue); buf.append('\n'); map.put(attrs[splitFeatureIndex].name + " [" + splitFeature + "] " + op, splitValue); } @Override public void visitLeaf(int output, double[] posteriori) { buf.append(targetAttr.toString(output)); } }); Assert.assertTrue(buf.length() > 0); Assert.assertFalse(map.isEmpty()); StringUtils.clear(buf); map.clear(); } }