我已经设置了LightSIDE插件并且可以正常运行，但是我不知道为什么我无法将数据保存到空文件中？这是我创建的一个简单结构。
- 活动是需要分类的数据列表。
- 我有三个类别，每个类别都有各自的类型。
- 我已经为每个类别定义了特定的单词列表。例如：Food（{Sushi, Food, Japan}，{Cap Jay, Food, Chinese}，{Jog, Sport, Running}，…）
这是我如何使用LightSIDE保存我的预测结果。
public void predictSectionType(String[] sections, List<String> activityList) { LightSideService currentLightsideHelper = new LightSideService(); Recipe newRecipe; // 初始化SIDEPlugin currentLightsideHelper.initSIDEPlugin(); try { // 加载带有提取特征和训练模型的Recipe ClassLoader myClassLoader = getClass().getClassLoader(); newRecipe = ConverterControl.readFromXML(new InputStreamReader(myClassLoader.getResourceAsStream("static/lightsideTrainingResult/trainingData.xml"))); // 预测结果数据 Recipe recipeToPredict = currentLightsideHelper.loadNewDocumentsFromCSV(sections); // 创建DocumentList和Recipe currentLightsideHelper.predictLabels(recipeToPredict, newRecipe); } catch (FileNotFoundException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } }我有一个LightSideService类，作为LightSIDE功能的总结类。
public class LightSideService { // 提取特征参数 final String featureTableName = "1Grams"; final int featureThreshold = 2; final String featureAnnotation = "Code"; final Type featureType = Type.NOMINAL; // 构建模型参数 final String trainingResultName = "Bayes_1Grams"; // 预测标签参数 final String predictionColumnName = featureAnnotation + "_Prediction"; final boolean showMaxScore = false; final boolean showDists = true; final boolean overwrite = false; final boolean useEvaluation = false; public DocumentListTableModel model = new DocumentListTableModel(null); public Map<String, Serializable> validationSettings = new TreeMap<String, Serializable>(); public Map<FeaturePlugin, Boolean> featurePlugins = new HashMap<FeaturePlugin, Boolean>(); public Map<LearningPlugin, Boolean> learningPlugins = new HashMap<LearningPlugin, Boolean>(); public Collection<ModelMetricPlugin> modelEvaluationPlugins = new ArrayList<ModelMetricPlugin>(); public Map<WrapperPlugin, Boolean> wrapperPlugins = new HashMap<WrapperPlugin, Boolean>(); // 初始化数据 ================================================== public void initSIDEPlugin() { SIDEPlugin[] featureExtractors = PluginManager.getSIDEPluginArrayByType("feature_hit_extractor"); boolean selected = true; for (SIDEPlugin fe : featureExtractors) { featurePlugins.put((FeaturePlugin) fe, selected); selected = false; } SIDEPlugin[] learners = PluginManager.getSIDEPluginArrayByType("model_builder"); for (SIDEPlugin le : learners) { learningPlugins.put((LearningPlugin) le, true); } SIDEPlugin[] tableEvaluations = PluginManager.getSIDEPluginArrayByType("model_evaluation"); for (SIDEPlugin fe : tableEvaluations) { modelEvaluationPlugins.add((ModelMetricPlugin) fe); } SIDEPlugin[] wrappers = PluginManager.getSIDEPluginArrayByType("learning_wrapper"); for (SIDEPlugin wr : wrappers) { wrapperPlugins.put((WrapperPlugin) wr, false); } } //用于训练模型，根据模型调整参数 public void initValidationSettings(Recipe currentRecipe) { validationSettings.put("testRecipe", currentRecipe); validationSettings.put("testSet", currentRecipe.getDocumentList()); validationSettings.put("annotation", "Age"); validationSettings.put("type", "CV"); validationSettings.put("foldMethod", "AUTO"); validationSettings.put("numFolds", 10); validationSettings.put("source", "RANDOM"); validationSettings.put("test", "true"); } // 加载CSV文档 ================================================== public Recipe loadNewDocumentsFromCSV(String filePath) { DocumentList testDocs; testDocs = chooseDocumentList(filePath); if (testDocs != null) { testDocs.guessTextAndAnnotationColumns(); Recipe currentRecipe = Recipe.fetchRecipe(); currentRecipe.setDocumentList(testDocs); return currentRecipe; } return null; } public Recipe loadNewDocumentsFromCSV(String[] rootCauseList) { DocumentList testDocs; testDocs = chooseDocumentList(rootCauseList); if (testDocs != null) { testDocs.guessTextAndAnnotationColumns(); Recipe currentRecipe = Recipe.fetchRecipe(); currentRecipe.setDocumentList(testDocs); return currentRecipe; } return null; } protected DocumentList chooseDocumentList(String filePath) { TreeSet<String> docNames = new TreeSet<String>(); docNames.add(filePath); try { DocumentList testDocs; Charset encoding = Charset.forName("UTF-8"); { testDocs = ImportController.makeDocumentList(docNames, encoding); } return testDocs; } catch (FileNotFoundException e) { e.printStackTrace(); } catch (Exception e) { e.printStackTrace(); } return null; } protected DocumentList chooseDocumentList(String[] rootCauseList) { try { DocumentList testDocs; testDocs = new DocumentList(); testDocs.setName("TestData.csv"); List<String> codes = new ArrayList(); List<String> roots = new ArrayList(); for (String s : rootCauseList) { codes.add(""); roots.add((s != null) ? s : ""); } testDocs.addAnnotation("Code", codes, false); testDocs.addAnnotation("Root Cause Failure Description", roots, false); return testDocs; } catch (Exception e) { e.printStackTrace(); } return null; } // 保存/加载XML ================================================== public void saveRecipeToXml(Recipe currentRecipe, String filePath) { File f = new File(filePath); try { ConverterControl.writeToXML(f, currentRecipe); } catch (Exception e) { e.printStackTrace(); } } public Recipe loadRecipeFromXml(String filePath) throws FileNotFoundException, IOException { Recipe currentRecipe = ConverterControl.loadRecipe(filePath); return currentRecipe; } // 提取特征 ================================================== public Recipe prepareBuildFeatureTable(Recipe currentRecipe) { // 添加特征插件 Collection<FeaturePlugin> plugins = new TreeSet<FeaturePlugin>(); for (FeaturePlugin plugin : featurePlugins.keySet()) { String pluginString = plugin.toString(); if (pluginString == "Basic Features" || pluginString == "Character N-Grams") { plugins.add(plugin); } } // 将插件生成到Recipe中 currentRecipe = Recipe.addPluginsToRecipe(currentRecipe, plugins); // 设置插件配置 OrderedPluginMap currentOrderedPluginMap = currentRecipe.getExtractors(); for (SIDEPlugin plugin : currentOrderedPluginMap.keySet()) { String pluginString = plugin.toString(); Map<String, String> currentConfigurations = currentOrderedPluginMap.get(plugin); if (pluginString == "Basic Features") { for (String s : currentConfigurations.keySet()) { if (s == "Unigrams" || s == "Bigrams" || s == "Trigrams" || s == "Count Occurences" || s == "Normalize N-Gram Counts" || s == "Stem N-Grams" || s == "Skip Stopwords in N-Grams") { currentConfigurations.put(s, "true"); } else { currentConfigurations.put(s, "false"); } } } else if (pluginString == "Character N-Grams") { for (String s : currentConfigurations.keySet()) { if (s == "Include Punctuation") { currentConfigurations.put(s, "true"); } else if (s == "minGram") { currentConfigurations.put(s, "3"); } else if (s == "maxGram") { currentConfigurations.put(s, "4"); } } currentConfigurations.put("Extract Only Within Words", "true"); } } // 构建特征表 currentRecipe = buildFeatureTable(currentRecipe, featureTableName, featureThreshold, featureAnnotation, featureType); return currentRecipe; } protected Recipe buildFeatureTable(Recipe currentRecipe, String name, int threshold, String annotation, Type type) { FeaturePlugin activeExtractor = null; try { Collection<FeatureHit> hits = new HashSet<FeatureHit>(); for (SIDEPlugin plug : currentRecipe.getExtractors().keySet()) { activeExtractor = (FeaturePlugin) plug; hits.addAll(activeExtractor.extractFeatureHits(currentRecipe.getDocumentList(), currentRecipe.getExtractors().get(plug))); } FeatureTable ft = new FeatureTable(currentRecipe.getDocumentList(), hits, threshold, annotation, type); ft.setName(name); currentRecipe.setFeatureTable(ft); } catch (Exception e) { System.err.println("特征提取失败"); e.printStackTrace(); } return currentRecipe; } // 构建模型 ================================================== public Recipe prepareBuildModel(Recipe currentRecipe) { try { // 获取学习插件 LearningPlugin learner = null; for (LearningPlugin plugin : learningPlugins.keySet()) { /* if (plugin.toString() == "Naive Bayes") */ if (plugin.toString() == "Logistic Regression") { learner = plugin; } } if (Boolean.TRUE.toString().equals(validationSettings.get("test"))) { if (validationSettings.get("type").equals("CV")) { validationSettings.put("testSet", currentRecipe.getDocumentList()); } } Map<String, String> settings = learner.generateConfigurationSettings(); currentRecipe = Recipe.addLearnerToRecipe(currentRecipe, learner, settings); currentRecipe.setValidationSettings(new TreeMap<String, Serializable>(validationSettings)); for (WrapperPlugin wrap : wrapperPlugins.keySet()) { if (wrapperPlugins.get(wrap)) { currentRecipe.addWrapper(wrap, wrap.generateConfigurationSettings()); } } buildModel(currentRecipe, validationSettings); } catch (Exception e) { e.printStackTrace(); } return currentRecipe; } protected void buildModel(Recipe currentRecipe, Map<String, Serializable> validationSettings) { try { FeatureTable currentFeatureTable = currentRecipe.getTrainingTable(); if (currentRecipe != null) { TrainingResult results = null; /* * if (validationSettings.get("type").equals("SUPPLY")) { * DocumentList test = (DocumentList) * validationSettings.get("testSet"); FeatureTable * extractTestFeatures = prepareTestFeatureTable(currentRecipe, * validationSettings, test); * validationSettings.put("testFeatureTable", * extractTestFeatures); * * // if we've already trained the exact same model, don't // do * it again. Just evaluate. Recipe cached = * checkForCachedModel(); if (cached != null) { results = * evaluateUsingCachedModel(currentFeatureTable, * extractTestFeatures, cached, currentRecipe); } } */ if (results == null) { results = currentRecipe.getLearner().train(currentFeatureTable, currentRecipe.getLearnerSettings(), validationSettings, currentRecipe.getWrappers()); } if (results != null) { currentRecipe.setTrainingResult(results); results.setName(trainingResultName); currentRecipe.setLearnerSettings(currentRecipe.getLearner().generateConfigurationSettings()); currentRecipe.setValidationSettings(new TreeMap<String, Serializable>(validationSettings)); } } } catch (Exception e) { e.printStackTrace(); } } protected static FeatureTable prepareTestFeatureTable(Recipe recipe, Map<String, Serializable> validationSettings, DocumentList test) { prepareDocuments(recipe, validationSettings, test); // 分配类别和注释。 Collection<FeatureHit> hits = new TreeSet<FeatureHit>(); OrderedPluginMap extractors = recipe.getExtractors(); for (SIDEPlugin plug : extractors.keySet()) { Collection<FeatureHit> extractorHits = ((FeaturePlugin) plug).extractFeatureHits(test, extractors.get(plug)); hits.addAll(extractorHits); } FeatureTable originalTable = recipe.getTrainingTable(); FeatureTable ft = new FeatureTable(test, hits, 0, originalTable.getAnnotation(), originalTable.getClassValueType()); for (SIDEPlugin plug : recipe.getFilters().keySet()) { ft = ((RestructurePlugin) plug).filterTestSet(originalTable, ft, recipe.getFilters().get(plug), recipe.getFilteredTable().getThreshold()); } ft.reconcileFeatures(originalTable.getFeatureSet()); return ft; } protected static Map<String, Serializable> prepareDocuments(Recipe currentRecipe, Map<String, Serializable> validationSettings, DocumentList test) throws IllegalStateException { DocumentList train = currentRecipe.getDocumentList(); try { test.setCurrentAnnotation(currentRecipe.getTrainingTable().getAnnotation(), currentRecipe.getTrainingTable().getClassValueType()); test.setTextColumns(new HashSet<String>(train.getTextColumns())); test.setDifferentiateTextColumns(train.getTextColumnsAreDifferentiated()); Collection<String> trainColumns = train.allAnnotations().keySet(); Collection<String> testColumns = test.allAnnotations().keySet(); if (!testColumns.containsAll(trainColumns)) { ArrayList<String> missing = new ArrayList<String>(trainColumns); missing.removeAll(testColumns); throw new java.lang.IllegalStateException("测试集注释与训练集不匹配。\n缺少的列：" + missing); } validationSettings.put("testSet", test); } catch (Exception e) { e.printStackTrace(); throw new java.lang.IllegalStateException("无法准备测试集。\n" + e.getMessage(), e); } return validationSettings; } //预测标签 ================================================== public void predictLabels(Recipe recipeToPredict, Recipe currentRecipe) { DocumentList newDocs = null; DocumentList originalDocs; if (useEvaluation) { originalDocs = recipeToPredict.getTrainingResult().getEvaluationTable().getDocumentList(); TrainingResult results = currentRecipe.getTrainingResult(); List<String> predictions = (List<String>) results.getPredictions(); newDocs = addLabelsToDocs(predictionColumnName, showDists, overwrite, originalDocs, results, predictions, currentRecipe.getTrainingTable()); } else { originalDocs = recipeToPredict.getDocumentList(); Predictor predictor = new Predictor(currentRecipe, predictionColumnName); newDocs = predictor.predict(originalDocs, predictionColumnName, showDists, overwrite); } // 预测标签结果 model.setDocumentList(newDocs); } protected DocumentList addLabelsToDocs(final String name, final boolean showDists, final boolean overwrite, DocumentList docs, TrainingResult results, List<String> predictions, FeatureTable currentFeatureTable) { Map<String, List<Double>> distributions = results.getDistributions(); DocumentList newDocs = docs.clone(); newDocs.addAnnotation(name, predictions, overwrite); if (distributions != null) { if (showDists) { for (String label : currentFeatureTable.getLabelArray()) { List<String> dist = new ArrayList<String>(); for (int i = 0; i < predictions.size(); i++) { dist.add(String.format("%.3f", distributions.get(label).get(i))); } newDocs.addAnnotation(name + "_" + label + "_score", dist, overwrite); } } } return newDocs; } // ==================================================}回答：
@[隐藏人名]，看起来上面的代码复制了edu.cmu.side.recipe包中的很多功能。然而，看起来你的predictSectionType()方法实际上并没有在任何地方输出模型的预测结果。
如果你确实想要使用训练好的模型在新数据上保存预测结果，请查看edu.cmu.side.recipe.Predictor类。它接受训练模型的路径作为输入，由scripts/predict.sh便捷脚本使用，但如果你需要以编程方式调用它，你可以重新使用它的主方法。
希望这对你有帮助！
