Examples with TermFeature org.jpmml.sparkml.TermFeature used on opensource projects

Example 1 with TermFeature

use of org.jpmml.sparkml.TermFeature in project jpmml-sparkml by jpmml.

the class CountVectorizerModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    CountVectorizerModel transformer = getTransformer();
    DocumentFeature documentFeature = (DocumentFeature) encoder.getOnlyFeature(transformer.getInputCol());
    ParameterField documentField = new ParameterField(FieldName.create("document"));
    ParameterField termField = new ParameterField(FieldName.create("term"));
    TextIndex textIndex = new TextIndex(documentField.getName()).setTokenize(Boolean.TRUE).setWordSeparatorCharacterRE(documentFeature.getWordSeparatorRE()).setLocalTermWeights(transformer.getBinary() ? TextIndex.LocalTermWeights.BINARY : null).setExpression(new FieldRef(termField.getName()));
    Set<DocumentFeature.StopWordSet> stopWordSets = documentFeature.getStopWordSets();
    for (DocumentFeature.StopWordSet stopWordSet : stopWordSets) {
        if (stopWordSet.isEmpty()) {
            continue;
        }
        DocumentBuilder documentBuilder = DOMUtil.createDocumentBuilder();
        String tokenRE;
        String wordSeparatorRE = documentFeature.getWordSeparatorRE();
        switch(wordSeparatorRE) {
            case "\\s+":
                tokenRE = "(^|\\s+)\\p{Punct}*(" + JOINER.join(stopWordSet) + ")\\p{Punct}*(\\s+|$)";
                break;
            case "\\W+":
                tokenRE = "(\\W+)(" + JOINER.join(stopWordSet) + ")(\\W+)";
                break;
            default:
                throw new IllegalArgumentException("Expected \"\\s+\" or \"\\W+\" as splitter regex pattern, got \"" + wordSeparatorRE + "\"");
        }
        InlineTable inlineTable = new InlineTable().addRows(DOMUtil.createRow(documentBuilder, Arrays.asList("string", "stem", "regex"), Arrays.asList(tokenRE, " ", "true")));
        TextIndexNormalization textIndexNormalization = new TextIndexNormalization().setCaseSensitive(stopWordSet.isCaseSensitive()).setRecursive(// Handles consecutive matches. See http://stackoverflow.com/a/25085385
        Boolean.TRUE).setInlineTable(inlineTable);
        textIndex.addTextIndexNormalizations(textIndexNormalization);
    }
    DefineFunction defineFunction = new DefineFunction("tf" + "@" + String.valueOf(CountVectorizerModelConverter.SEQUENCE.getAndIncrement()), OpType.CONTINUOUS, null).setDataType(DataType.INTEGER).addParameterFields(documentField, termField).setExpression(textIndex);
    encoder.addDefineFunction(defineFunction);
    List<Feature> result = new ArrayList<>();
    String[] vocabulary = transformer.vocabulary();
    for (int i = 0; i < vocabulary.length; i++) {
        String term = vocabulary[i];
        if (TermUtil.hasPunctuation(term)) {
            throw new IllegalArgumentException(term);
        }
        result.add(new TermFeature(encoder, defineFunction, documentFeature, term));
    }
    return result;
}

Example 2 with TermFeature

use of org.jpmml.sparkml.TermFeature in project jpmml-sparkml by jpmml.

the class IDFModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    IDFModel transformer = getTransformer();
    List<Feature> features = encoder.getFeatures(transformer.getInputCol());
    Vector idf = transformer.idf();
    if (idf.size() != features.size()) {
        throw new IllegalArgumentException();
    }
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < features.size(); i++) {
        Feature feature = features.get(i);
        TermFeature termFeature = (TermFeature) feature;
        result.add(termFeature.toWeightedTermFeature(idf.apply(i)));
    }
    return result;
}