Examples with StringIndexerModel org.apache.spark.ml.feature.StringIndexerModel used on opensource projects

Example 1 with StringIndexerModel

use of org.apache.spark.ml.feature.StringIndexerModel in project mmtf-spark by sbl-sdsc.

the class SparkMultiClassClassifier method fit.

/**
 * Dataset must at least contain the following two columns:
 * label: the class labels
 * features: feature vector
 * @param data
 * @return map with metrics
 */
public Map<String, String> fit(Dataset<Row> data) {
    int classCount = (int) data.select(label).distinct().count();
    StringIndexerModel labelIndexer = new StringIndexer().setInputCol(label).setOutputCol("indexedLabel").fit(data);
    // Split the data into training and test sets (30% held out for testing)
    Dataset<Row>[] splits = data.randomSplit(new double[] { 1.0 - testFraction, testFraction }, seed);
    Dataset<Row> trainingData = splits[0];
    Dataset<Row> testData = splits[1];
    String[] labels = labelIndexer.labels();
    System.out.println();
    System.out.println("Class\tTrain\tTest");
    for (String l : labels) {
        System.out.println(l + "\t" + trainingData.select(label).filter(label + " = '" + l + "'").count() + "\t" + testData.select(label).filter(label + " = '" + l + "'").count());
    }
    // Set input columns
    predictor.setLabelCol("indexedLabel").setFeaturesCol("features");
    // Convert indexed labels back to original labels.
    IndexToString labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels());
    // Chain indexers and forest in a Pipeline
    Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] { labelIndexer, predictor, labelConverter });
    // Train model. This also runs the indexers.
    PipelineModel model = pipeline.fit(trainingData);
    // Make predictions.
    Dataset<Row> predictions = model.transform(testData).cache();
    // Display some sample predictions
    System.out.println();
    System.out.println("Sample predictions: " + predictor.getClass().getSimpleName());
    predictions.sample(false, 0.1, seed).show(25);
    predictions = predictions.withColumnRenamed(label, "stringLabel");
    predictions = predictions.withColumnRenamed("indexedLabel", label);
    // collect metrics
    Dataset<Row> pred = predictions.select("prediction", label);
    Map<String, String> metrics = new LinkedHashMap<>();
    metrics.put("Method", predictor.getClass().getSimpleName());
    if (classCount == 2) {
        BinaryClassificationMetrics b = new BinaryClassificationMetrics(pred);
        metrics.put("AUC", Float.toString((float) b.areaUnderROC()));
    }
    MulticlassMetrics m = new MulticlassMetrics(pred);
    metrics.put("F", Float.toString((float) m.weightedFMeasure()));
    metrics.put("Accuracy", Float.toString((float) m.accuracy()));
    metrics.put("Precision", Float.toString((float) m.weightedPrecision()));
    metrics.put("Recall", Float.toString((float) m.weightedRecall()));
    metrics.put("False Positive Rate", Float.toString((float) m.weightedFalsePositiveRate()));
    metrics.put("True Positive Rate", Float.toString((float) m.weightedTruePositiveRate()));
    metrics.put("", "\nConfusion Matrix\n" + Arrays.toString(labels) + "\n" + m.confusionMatrix().toString());
    return metrics;
}

Example 2 with StringIndexerModel

use of org.apache.spark.ml.feature.StringIndexerModel in project jpmml-sparkml by jpmml.

the class StringIndexerModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    StringIndexerModel transformer = getTransformer();
    Feature feature = encoder.getOnlyFeature(transformer.getInputCol());
    List<String> categories = new ArrayList<>();
    categories.addAll(Arrays.asList(transformer.labels()));
    String handleInvalid = transformer.getHandleInvalid();
    Field<?> field = encoder.toCategorical(feature.getName(), categories);
    if (field instanceof DataField) {
        DataField dataField = (DataField) field;
        InvalidValueTreatmentMethod invalidValueTreatmentMethod;
        switch(handleInvalid) {
            case "keep":
                invalidValueTreatmentMethod = InvalidValueTreatmentMethod.AS_IS;
                break;
            case "error":
                invalidValueTreatmentMethod = InvalidValueTreatmentMethod.RETURN_INVALID;
                break;
            default:
                throw new IllegalArgumentException(handleInvalid);
        }
        InvalidValueDecorator invalidValueDecorator = new InvalidValueDecorator().setInvalidValueTreatment(invalidValueTreatmentMethod);
        encoder.addDecorator(dataField.getName(), invalidValueDecorator);
    } else if (field instanceof DerivedField) {
    // Ignored
    } else {
        throw new IllegalArgumentException();
    }
    switch(handleInvalid) {
        case "keep":
            Apply setApply = PMMLUtil.createApply("isIn", feature.ref());
            for (String category : categories) {
                setApply.addExpressions(PMMLUtil.createConstant(category, feature.getDataType()));
            }
            categories.add(StringIndexerModelConverter.LABEL_UNKNOWN);
            Apply apply = PMMLUtil.createApply("if", setApply, feature.ref(), PMMLUtil.createConstant(StringIndexerModelConverter.LABEL_UNKNOWN, DataType.STRING));
            field = encoder.createDerivedField(FeatureUtil.createName("handleInvalid", feature), OpType.CATEGORICAL, feature.getDataType(), apply);
            break;
        default:
            break;
    }
    return Collections.<Feature>singletonList(new CategoricalFeature(encoder, field, categories));
}