use of edu.neu.ccs.pyramid.util.Progress in project pyramid by cheng-li.
the class App2 method report.
static void report(Config config, String dataName, Logger logger) throws Exception {
logger.info("generating reports for data set " + dataName);
String output = config.getString("output.folder");
String modelName = "model_app3";
File analysisFolder = new File(new File(output, "reports_app3"), dataName + "_reports");
analysisFolder.mkdirs();
FileUtils.cleanDirectory(analysisFolder);
IMLGradientBoosting boosting = IMLGradientBoosting.deserialize(new File(output, modelName));
String predictTarget = config.getString("predict.target");
PluginPredictor<IMLGradientBoosting> pluginPredictorTmp = null;
switch(predictTarget) {
case "subsetAccuracy":
pluginPredictorTmp = new SubsetAccPredictor(boosting);
break;
case "hammingLoss":
pluginPredictorTmp = new HammingPredictor(boosting);
break;
case "instanceFMeasure":
pluginPredictorTmp = new InstanceF1Predictor(boosting);
break;
case "macroFMeasure":
TunedMarginalClassifier tunedMarginalClassifier = (TunedMarginalClassifier) Serialization.deserialize(new File(output, "predictor_macro_f"));
pluginPredictorTmp = new MacroF1Predictor(boosting, tunedMarginalClassifier);
break;
default:
throw new IllegalArgumentException("unknown prediction target measure " + predictTarget);
}
// just to make Lambda expressions happy
final PluginPredictor<IMLGradientBoosting> pluginPredictor = pluginPredictorTmp;
MultiLabelClfDataSet dataSet = loadData(config, dataName);
MLMeasures mlMeasures = new MLMeasures(pluginPredictor, dataSet);
mlMeasures.getMacroAverage().setLabelTranslator(boosting.getLabelTranslator());
logger.info("performance on dataset " + dataName);
logger.info(mlMeasures.toString());
boolean simpleCSV = true;
if (simpleCSV) {
logger.info("start generating simple CSV report");
double probThreshold = config.getDouble("report.classProbThreshold");
File csv = new File(analysisFolder, "report.csv");
List<String> strs = IntStream.range(0, dataSet.getNumDataPoints()).parallel().mapToObj(i -> IMLGBInspector.simplePredictionAnalysis(boosting, pluginPredictor, dataSet, i, probThreshold)).collect(Collectors.toList());
try (BufferedWriter bw = new BufferedWriter(new FileWriter(csv))) {
for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
String str = strs.get(i);
bw.write(str);
}
}
logger.info("finish generating simple CSV report");
}
boolean rulesToJson = config.getBoolean("report.showPredictionDetail");
if (rulesToJson) {
logger.info("start writing rules to json");
int ruleLimit = config.getInt("report.rule.limit");
int numDocsPerFile = config.getInt("report.numDocsPerFile");
int numFiles = (int) Math.ceil((double) dataSet.getNumDataPoints() / numDocsPerFile);
double probThreshold = config.getDouble("report.classProbThreshold");
int labelSetLimit = config.getInt("report.labelSetLimit");
IntStream.range(0, numFiles).forEach(i -> {
int start = i * numDocsPerFile;
int end = start + numDocsPerFile;
List<MultiLabelPredictionAnalysis> partition = IntStream.range(start, Math.min(end, dataSet.getNumDataPoints())).parallel().mapToObj(a -> IMLGBInspector.analyzePrediction(boosting, pluginPredictor, dataSet, a, ruleLimit, labelSetLimit, probThreshold)).collect(Collectors.toList());
ObjectMapper mapper = new ObjectMapper();
String file = "report_" + (i + 1) + ".json";
try {
mapper.writeValue(new File(analysisFolder, file), partition);
} catch (IOException e) {
e.printStackTrace();
}
logger.info("progress = " + Progress.percentage(i + 1, numFiles));
});
logger.info("finish writing rules to json");
}
boolean dataInfoToJson = true;
if (dataInfoToJson) {
logger.info("start writing data info to json");
Set<String> modelLabels = IntStream.range(0, boosting.getNumClasses()).mapToObj(i -> boosting.getLabelTranslator().toExtLabel(i)).collect(Collectors.toSet());
Set<String> dataSetLabels = DataSetUtil.gatherLabels(dataSet).stream().map(i -> dataSet.getLabelTranslator().toExtLabel(i)).collect(Collectors.toSet());
JsonGenerator jsonGenerator = new JsonFactory().createGenerator(new File(analysisFolder, "data_info.json"), JsonEncoding.UTF8);
jsonGenerator.writeStartObject();
jsonGenerator.writeStringField("dataSet", dataName);
jsonGenerator.writeNumberField("numClassesInModel", boosting.getNumClasses());
jsonGenerator.writeNumberField("numClassesInDataSet", dataSetLabels.size());
jsonGenerator.writeNumberField("numClassesInModelDataSetCombined", dataSet.getNumClasses());
Set<String> modelNotDataLabels = SetUtil.complement(modelLabels, dataSetLabels);
Set<String> dataNotModelLabels = SetUtil.complement(dataSetLabels, modelLabels);
jsonGenerator.writeNumberField("numClassesInDataSetButNotModel", dataNotModelLabels.size());
jsonGenerator.writeNumberField("numClassesInModelButNotDataSet", modelNotDataLabels.size());
jsonGenerator.writeArrayFieldStart("classesInDataSetButNotModel");
for (String label : dataNotModelLabels) {
jsonGenerator.writeObject(label);
}
jsonGenerator.writeEndArray();
jsonGenerator.writeArrayFieldStart("classesInModelButNotDataSet");
for (String label : modelNotDataLabels) {
jsonGenerator.writeObject(label);
}
jsonGenerator.writeEndArray();
jsonGenerator.writeNumberField("labelCardinality", dataSet.labelCardinality());
jsonGenerator.writeEndObject();
jsonGenerator.close();
logger.info("finish writing data info to json");
}
boolean modelConfigToJson = true;
if (modelConfigToJson) {
logger.info("start writing model config to json");
ObjectMapper objectMapper = new ObjectMapper();
objectMapper.writeValue(new File(analysisFolder, "model_config.json"), config);
logger.info("finish writing model config to json");
}
boolean dataConfigToJson = true;
if (dataConfigToJson) {
logger.info("start writing data config to json");
File dataConfigFile = Paths.get(config.getString("input.folder"), "data_sets", dataName, "data_config.json").toFile();
if (dataConfigFile.exists()) {
FileUtils.copyFileToDirectory(dataConfigFile, analysisFolder);
}
logger.info("finish writing data config to json");
}
boolean performanceToJson = true;
if (performanceToJson) {
ObjectMapper objectMapper = new ObjectMapper();
objectMapper.writeValue(new File(analysisFolder, "performance.json"), mlMeasures);
}
boolean individualPerformance = true;
if (individualPerformance) {
logger.info("start writing individual label performance to json");
ObjectMapper objectMapper = new ObjectMapper();
objectMapper.writeValue(new File(analysisFolder, "individual_performance.json"), mlMeasures.getMacroAverage());
logger.info("finish writing individual label performance to json");
}
logger.info("reports generated");
}
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