什么是画像平台实时预测服务？

// 存储特征映射关系
private static Map<String, Integer> featureMap = new HashMap<>();
private static Booster booster;
// 读取并加载特征文件数据，特征文件第一列是特征ID，第二列是特征数值
// 示例：100 phone_brand:iphone
private static void loadFeature(List<Resource> featurePath) throws IOException {
 for (Resource input : featurePath) {
 BufferedReader reader = new BufferedReader(new InputStreamReader(input.getInputStream()));
 String line;
 while ((line = reader.readLine()) != null) {
 String[] items = line.split("\t");
 featureMap.put(items[1].toLowerCase(), Integer.valueOf(items[0]));
 }
 reader.close();
 }
}
@Data
@AllArgsConstructor
public static class FeatureElement {
 private String featureName;  // 特征名称
 private float featureValue;  // 特征数值
}
// 加载模型文件
private static void loadModel(Resource modelPath) throws XGBoostError, IOException {
 booster = XGBoost.loadModel(modelPath.getInputStream());
}
public static void main(String[] args) throws IOException, XGBoostError {
 byte[] featureBytes = null; // 特征文件bytes
 byte[] modelBytes = null; // 模型文件bytes
 Resource featureResource = new ByteArrayResource(featureBytes);
 Resource modelResource = new ByteArrayResource(modelBytes);
 // 加载模型文件和特征文件
 loadFeature(Lists.newArrayList(featureResource));
 loadModel(modelResource);
 // 构建特征数据，特征数据来源自调用方或者从实时特征库中查询获取
 List<FeatureElement> marrayOrNotFeature = Lists.newArrayList();
 marrayOrNotFeature.add(new FeatureElement("特征1", 100.0f));
 marrayOrNotFeature.add(new FeatureElement("特征1", 20.0f));
 // 构建预测使用的特征数据
 List<Integer> featureIds = Lists.newArrayList();
 List<Float> featureValues = Lists.newArrayList();
 for (FeatureElement element : marrayOrNotFeature) {
 String featureName = element.getFeatureName().toLowerCase();
 float featureVal = element.getFeatureValue();
 Integer featureId = featureMap.get(featureName);
 if (featureId != null) {
 featureIds.add(featureId);
 featureValues.add(featureVal);
 }
 }
 // 通过模型预测结果数值
 int numColumn = featureIds.size();
 int[] colIndex = featureIds.stream().mapToInt(i -> i).toArray();
 long[] rowHeaders = new long[] {0, colIndex.length};
 float[] data = Floats.toArray(Doubles.asList(featureValues.stream().mapToDouble(i -> i).toArray()));
 DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR, numColumn);
 // predicts即不同预测结果概率值，根据概率值大小即可确定预测结果
 float[][] predicts = booster.predict(dmat, false, 0);
 dmat.dispose();
}