Examples with BasicFloatNetwork ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork used on opensource projects

Example 1 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class VarSelectMapper method setup.

/**
 * Do initialization like ModelConfig and ColumnConfig loading, model loading and others like input or output number
 * loading.
 */
@Override
protected void setup(Context context) throws IOException, InterruptedException {
    loadConfigFiles(context);
    loadModel();
    // Copy mode to here
    cacheNetwork = copy((BasicFloatNetwork) model);
    this.filterBy = context.getConfiguration().get(Constants.SHIFU_VARSELECT_FILTEROUT_TYPE, Constants.FILTER_BY_SE);
    int[] inputOutputIndex = DTrainUtils.getInputOutputCandidateCounts(modelConfig.getNormalizeType(), columnConfigList);
    this.inputNodeCount = inputOutputIndex[0] == 0 ? inputOutputIndex[2] : inputOutputIndex[0];
    if (model instanceof BasicFloatNetwork) {
        this.inputs = new double[((BasicFloatNetwork) model).getFeatureSet().size()];
        this.featureSet = ((BasicFloatNetwork) model).getFeatureSet();
    } else {
        this.inputs = new double[this.inputNodeCount];
    }
    boolean isAfterVarSelect = (inputOutputIndex[0] != 0);
    // cache all feature list for sampling features
    if (this.featureSet == null || this.featureSet.size() == 0) {
        this.featureSet = new HashSet<Integer>(NormalUtils.getAllFeatureList(columnConfigList, isAfterVarSelect));
        this.inputs = new double[this.featureSet.size()];
    }
    if (inputs.length != this.inputNodeCount) {
        throw new IllegalArgumentException("Model input count " + model.getInputCount() + " is inconsistent with input size " + this.inputNodeCount + ".");
    }
    this.outputs = new double[inputOutputIndex[1]];
    this.columnIndexes = new long[this.inputs.length];
    this.inputsMLData = new BasicMLData(this.inputs.length);
    this.outputKey = new LongWritable();
    LOG.info("Filter by is {}", filterBy);
    // create Splitter
    String delimiter = context.getConfiguration().get(Constants.SHIFU_OUTPUT_DATA_DELIMITER);
    this.splitter = MapReduceUtils.generateShifuOutputSplitter(delimiter);
}

Example 2 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class Scorer method scoreNsData.

public ScoreObject scoreNsData(MLDataPair inputPair, Map<NSColumn, String> rawNsDataMap) {
    if (inputPair == null && !this.alg.equalsIgnoreCase(NNConstants.NN_ALG_NAME)) {
        inputPair = NormalUtils.assembleNsDataPair(binCategoryMap, noVarSelect, modelConfig, selectedColumnConfigList, rawNsDataMap, cutoff, alg);
    }
    // clear cache
    this.cachedNormDataPair.clear();
    final MLDataPair pair = inputPair;
    List<MLData> modelResults = new ArrayList<MLData>();
    List<Callable<MLData>> tasks = null;
    if (this.multiThread) {
        tasks = new ArrayList<Callable<MLData>>();
    }
    for (final BasicML model : models) {
        // TODO, check if no need 'if' condition and refactor two if for loops please
        if (model instanceof BasicFloatNetwork || model instanceof NNModel) {
            final BasicFloatNetwork network = (model instanceof BasicFloatNetwork) ? (BasicFloatNetwork) model : ((NNModel) model).getIndependentNNModel().getBasicNetworks().get(0);
            String cacheKey = featureSetToString(network.getFeatureSet());
            MLDataPair dataPair = cachedNormDataPair.get(cacheKey);
            if (dataPair == null) {
                dataPair = NormalUtils.assembleNsDataPair(binCategoryMap, noVarSelect, modelConfig, selectedColumnConfigList, rawNsDataMap, cutoff, alg, network.getFeatureSet());
                cachedNormDataPair.put(cacheKey, dataPair);
            }
            final MLDataPair networkPair = dataPair;
            /*
                 * if(network.getFeatureSet().size() != networkPair.getInput().size()) {
                 * log.error("Network and input size mismatch: Network Size = " + network.getFeatureSet().size()
                 * + "; Input Size = " + networkPair.getInput().size());
                 * continue;
                 * }
                 */
            if (System.currentTimeMillis() % 1000 == 0L) {
                log.info("Network input count = {}, while input size = {}", network.getInputCount(), networkPair.getInput().size());
            }
            final int fnlOutputHiddenLayerIndex = outputHiddenLayerIndex;
            Callable<MLData> callable = new Callable<MLData>() {

                @Override
                public MLData call() {
                    MLData finalOutput = network.compute(networkPair.getInput());
                    if (fnlOutputHiddenLayerIndex == 0) {
                        return finalOutput;
                    }
                    // append output values in hidden layer
                    double[] hiddenOutputs = network.getLayerOutput(fnlOutputHiddenLayerIndex);
                    double[] outputs = new double[finalOutput.getData().length + hiddenOutputs.length];
                    System.arraycopy(finalOutput.getData(), 0, outputs, 0, finalOutput.getData().length);
                    System.arraycopy(hiddenOutputs, 0, outputs, finalOutput.getData().length, hiddenOutputs.length);
                    return new BasicMLData(outputs);
                }
            };
            if (multiThread) {
                tasks.add(callable);
            } else {
                try {
                    modelResults.add(callable.call());
                } catch (Exception e) {
                    log.error("error in model evaluation", e);
                }
            }
        } else if (model instanceof BasicNetwork) {
            final BasicNetwork network = (BasicNetwork) model;
            final MLDataPair networkPair = NormalUtils.assembleNsDataPair(binCategoryMap, noVarSelect, modelConfig, columnConfigList, rawNsDataMap, cutoff, alg, null);
            Callable<MLData> callable = new Callable<MLData>() {

                @Override
                public MLData call() {
                    return network.compute(networkPair.getInput());
                }
            };
            if (multiThread) {
                tasks.add(callable);
            } else {
                try {
                    modelResults.add(callable.call());
                } catch (Exception e) {
                    log.error("error in model evaluation", e);
                }
            }
        } else if (model instanceof SVM) {
            final SVM svm = (SVM) model;
            if (svm.getInputCount() != pair.getInput().size()) {
                log.error("SVM and input size mismatch: SVM Size = " + svm.getInputCount() + "; Input Size = " + pair.getInput().size());
                continue;
            }
            Callable<MLData> callable = new Callable<MLData>() {

                @Override
                public MLData call() {
                    return svm.compute(pair.getInput());
                }
            };
            if (multiThread) {
                tasks.add(callable);
            } else {
                try {
                    modelResults.add(callable.call());
                } catch (Exception e) {
                    log.error("error in model evaluation", e);
                }
            }
        } else if (model instanceof LR) {
            final LR lr = (LR) model;
            if (lr.getInputCount() != pair.getInput().size()) {
                log.error("LR and input size mismatch: LR Size = " + lr.getInputCount() + "; Input Size = " + pair.getInput().size());
                continue;
            }
            Callable<MLData> callable = new Callable<MLData>() {

                @Override
                public MLData call() {
                    return lr.compute(pair.getInput());
                }
            };
            if (multiThread) {
                tasks.add(callable);
            } else {
                try {
                    modelResults.add(callable.call());
                } catch (Exception e) {
                    log.error("error in model evaluation", e);
                }
            }
        } else if (model instanceof TreeModel) {
            final TreeModel tm = (TreeModel) model;
            if (tm.getInputCount() != pair.getInput().size()) {
                throw new RuntimeException("GBDT and input size mismatch: tm input Size = " + tm.getInputCount() + "; data input Size = " + pair.getInput().size());
            }
            Callable<MLData> callable = new Callable<MLData>() {

                @Override
                public MLData call() {
                    MLData result = tm.compute(pair.getInput());
                    return result;
                }
            };
            if (multiThread) {
                tasks.add(callable);
            } else {
                try {
                    modelResults.add(callable.call());
                } catch (Exception e) {
                    log.error("error in model evaluation", e);
                }
            }
        } else if (model instanceof GenericModel) {
            Callable<MLData> callable = new Callable<MLData>() {

                @Override
                public MLData call() {
                    return ((GenericModel) model).compute(pair.getInput());
                }
            };
            if (multiThread) {
                tasks.add(callable);
            } else {
                try {
                    modelResults.add(callable.call());
                } catch (Exception e) {
                    log.error("error in model evaluation", e);
                }
            }
        } else {
            throw new RuntimeException("unsupport models");
        }
    }
    List<Double> scores = new ArrayList<Double>();
    List<Integer> rfTreeSizeList = new ArrayList<Integer>();
    SortedMap<String, Double> hiddenOutputs = null;
    if (CollectionUtils.isNotEmpty(modelResults) || CollectionUtils.isNotEmpty(tasks)) {
        int modelSize = modelResults.size() > 0 ? modelResults.size() : tasks.size();
        if (modelSize != this.models.size()) {
            log.error("Get model results size doesn't match with models size.");
            return null;
        }
        if (multiThread) {
            modelResults = this.executorManager.submitTasksAndWaitResults(tasks);
        } else {
        // not multi-thread, modelResults is directly being populated in callable.call
        }
        if (this.outputHiddenLayerIndex != 0) {
            hiddenOutputs = new TreeMap<String, Double>(new Comparator<String>() {

                @Override
                public int compare(String o1, String o2) {
                    String[] split1 = o1.split("_");
                    String[] split2 = o2.split("_");
                    int model1Index = Integer.parseInt(split1[1]);
                    int model2Index = Integer.parseInt(split2[1]);
                    if (model1Index > model2Index) {
                        return 1;
                    } else if (model1Index < model2Index) {
                        return -1;
                    } else {
                        int hidden1Index = Integer.parseInt(split1[2]);
                        int hidden2Index = Integer.parseInt(split2[2]);
                        if (hidden1Index > hidden2Index) {
                            return 1;
                        } else if (hidden1Index < hidden2Index) {
                            return -1;
                        } else {
                            int hidden11Index = Integer.parseInt(split1[3]);
                            int hidden22Index = Integer.parseInt(split2[3]);
                            return Integer.valueOf(hidden11Index).compareTo(Integer.valueOf(hidden22Index));
                        }
                    }
                }
            });
        }
        for (int i = 0; i < this.models.size(); i++) {
            BasicML model = this.models.get(i);
            MLData score = modelResults.get(i);
            if (model instanceof BasicNetwork || model instanceof NNModel) {
                if (modelConfig != null && modelConfig.isRegression()) {
                    scores.add(toScore(score.getData(0)));
                    if (this.outputHiddenLayerIndex != 0) {
                        for (int j = 1; j < score.getData().length; j++) {
                            hiddenOutputs.put("model_" + i + "_" + this.outputHiddenLayerIndex + "_" + (j - 1), score.getData()[j]);
                        }
                    }
                } else if (modelConfig != null && modelConfig.isClassification() && modelConfig.getTrain().isOneVsAll()) {
                    // if one vs all classification
                    scores.add(toScore(score.getData(0)));
                } else {
                    double[] outputs = score.getData();
                    for (double d : outputs) {
                        scores.add(toScore(d));
                    }
                }
            } else if (model instanceof SVM) {
                scores.add(toScore(score.getData(0)));
            } else if (model instanceof LR) {
                scores.add(toScore(score.getData(0)));
            } else if (model instanceof TreeModel) {
                if (modelConfig.isClassification() && !modelConfig.getTrain().isOneVsAll()) {
                    double[] scoreArray = score.getData();
                    for (double sc : scoreArray) {
                        scores.add(sc);
                    }
                } else {
                    // if one vs all multiple classification or regression
                    scores.add(toScore(score.getData(0)));
                }
                final TreeModel tm = (TreeModel) model;
                // regression for RF
                if (!tm.isClassfication() && !tm.isGBDT()) {
                    rfTreeSizeList.add(tm.getTrees().size());
                }
            } else if (model instanceof GenericModel) {
                scores.add(toScore(score.getData(0)));
            } else {
                throw new RuntimeException("unsupport models");
            }
        }
    }
    Integer tag = Constants.DEFAULT_IDEAL_VALUE;
    if (scores.size() == 0 && System.currentTimeMillis() % 100 == 0) {
        log.warn("No Scores Calculated...");
    }
    return new ScoreObject(scores, tag, rfTreeSizeList, hiddenOutputs);
}

Example 3 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class TrainModelProcessor method runDistributedTrain.

protected int runDistributedTrain() throws IOException, InterruptedException, ClassNotFoundException {
    LOG.info("Started {}distributed training.", isDryTrain ? "dry " : "");
    int status = 0;
    Configuration conf = new Configuration();
    SourceType sourceType = super.getModelConfig().getDataSet().getSource();
    final List<String> args = new ArrayList<String>();
    GridSearch gs = new GridSearch(modelConfig.getTrain().getParams(), modelConfig.getTrain().getGridConfigFileContent());
    prepareCommonParams(gs.hasHyperParam(), args, sourceType);
    String alg = super.getModelConfig().getTrain().getAlgorithm();
    // add tmp models folder to config
    FileSystem fileSystem = ShifuFileUtils.getFileSystemBySourceType(sourceType);
    Path tmpModelsPath = fileSystem.makeQualified(new Path(super.getPathFinder().getPathBySourceType(new Path(Constants.TMP, Constants.DEFAULT_MODELS_TMP_FOLDER), sourceType)));
    args.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.SHIFU_TMP_MODELS_FOLDER, tmpModelsPath.toString()));
    int baggingNum = isForVarSelect ? 1 : super.getModelConfig().getBaggingNum();
    if (modelConfig.isClassification()) {
        int classes = modelConfig.getTags().size();
        if (classes == 2) {
            // binary classification, only need one job
            baggingNum = 1;
        } else {
            if (modelConfig.getTrain().isOneVsAll()) {
                // one vs all multiple classification, we need multiple bagging jobs to do ONEVSALL
                baggingNum = modelConfig.getTags().size();
            } else {
            // native classification, using bagging from setting job, no need set here
            }
        }
        if (baggingNum != super.getModelConfig().getBaggingNum()) {
            LOG.warn("'train:baggingNum' is set to {} because of ONEVSALL multiple classification.", baggingNum);
        }
    }
    boolean isKFoldCV = false;
    Integer kCrossValidation = this.modelConfig.getTrain().getNumKFold();
    if (kCrossValidation != null && kCrossValidation > 0) {
        isKFoldCV = true;
        baggingNum = modelConfig.getTrain().getNumKFold();
        if (baggingNum != super.getModelConfig().getBaggingNum() && gs.hasHyperParam()) {
            // if it is grid search mode, then kfold mode is disabled
            LOG.warn("'train:baggingNum' is set to {} because of k-fold cross validation is enabled by 'numKFold' not -1.", baggingNum);
        }
    }
    long start = System.currentTimeMillis();
    boolean isParallel = Boolean.valueOf(Environment.getProperty(Constants.SHIFU_DTRAIN_PARALLEL, SHIFU_DEFAULT_DTRAIN_PARALLEL)).booleanValue();
    GuaguaMapReduceClient guaguaClient;
    int[] inputOutputIndex = DTrainUtils.getInputOutputCandidateCounts(modelConfig.getNormalizeType(), this.columnConfigList);
    int inputNodeCount = inputOutputIndex[0] == 0 ? inputOutputIndex[2] : inputOutputIndex[0];
    int candidateCount = inputOutputIndex[2];
    boolean isAfterVarSelect = (inputOutputIndex[0] != 0);
    // cache all feature list for sampling features
    List<Integer> allFeatures = NormalUtils.getAllFeatureList(this.columnConfigList, isAfterVarSelect);
    if (modelConfig.getNormalize().getIsParquet()) {
        guaguaClient = new GuaguaParquetMapReduceClient();
        // set required field list to make sure we only load selected columns.
        RequiredFieldList requiredFieldList = new RequiredFieldList();
        boolean hasCandidates = CommonUtils.hasCandidateColumns(columnConfigList);
        for (ColumnConfig columnConfig : super.columnConfigList) {
            if (columnConfig.isTarget()) {
                requiredFieldList.add(new RequiredField(columnConfig.getColumnName(), columnConfig.getColumnNum(), null, DataType.FLOAT));
            } else {
                if (inputNodeCount == candidateCount) {
                    // no any variables are selected
                    if (!columnConfig.isMeta() && !columnConfig.isTarget() && CommonUtils.isGoodCandidate(columnConfig, hasCandidates)) {
                        requiredFieldList.add(new RequiredField(columnConfig.getColumnName(), columnConfig.getColumnNum(), null, DataType.FLOAT));
                    }
                } else {
                    if (!columnConfig.isMeta() && !columnConfig.isTarget() && columnConfig.isFinalSelect()) {
                        requiredFieldList.add(new RequiredField(columnConfig.getColumnName(), columnConfig.getColumnNum(), null, DataType.FLOAT));
                    }
                }
            }
        }
        // weight is added manually
        requiredFieldList.add(new RequiredField("weight", columnConfigList.size(), null, DataType.DOUBLE));
        args.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, "parquet.private.pig.required.fields", serializeRequiredFieldList(requiredFieldList)));
        args.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, "parquet.private.pig.column.index.access", "true"));
    } else {
        guaguaClient = new GuaguaMapReduceClient();
    }
    int parallelNum = Integer.parseInt(Environment.getProperty(CommonConstants.SHIFU_TRAIN_BAGGING_INPARALLEL, "5"));
    int parallelGroups = 1;
    if (gs.hasHyperParam()) {
        parallelGroups = (gs.getFlattenParams().size() % parallelNum == 0 ? gs.getFlattenParams().size() / parallelNum : gs.getFlattenParams().size() / parallelNum + 1);
        baggingNum = gs.getFlattenParams().size();
        LOG.warn("'train:baggingNum' is set to {} because of grid search enabled by settings in 'train#params'.", gs.getFlattenParams().size());
    } else {
        parallelGroups = baggingNum % parallelNum == 0 ? baggingNum / parallelNum : baggingNum / parallelNum + 1;
    }
    LOG.info("Distributed trainning with baggingNum: {}", baggingNum);
    List<String> progressLogList = new ArrayList<String>(baggingNum);
    boolean isOneJobNotContinuous = false;
    for (int j = 0; j < parallelGroups; j++) {
        int currBags = baggingNum;
        if (gs.hasHyperParam()) {
            if (j == parallelGroups - 1) {
                currBags = gs.getFlattenParams().size() % parallelNum == 0 ? parallelNum : gs.getFlattenParams().size() % parallelNum;
            } else {
                currBags = parallelNum;
            }
        } else {
            if (j == parallelGroups - 1) {
                currBags = baggingNum % parallelNum == 0 ? parallelNum : baggingNum % parallelNum;
            } else {
                currBags = parallelNum;
            }
        }
        for (int k = 0; k < currBags; k++) {
            int i = j * parallelNum + k;
            if (gs.hasHyperParam()) {
                LOG.info("Start the {}th grid search job with params: {}", i, gs.getParams(i));
            } else if (isKFoldCV) {
                LOG.info("Start the {}th k-fold cross validation job with params.", i);
            }
            List<String> localArgs = new ArrayList<String>(args);
            // set name for each bagging job.
            localArgs.add("-n");
            localArgs.add(String.format("Shifu Master-Workers %s Training Iteration: %s id:%s", alg, super.getModelConfig().getModelSetName(), i));
            LOG.info("Start trainer with id: {}", i);
            String modelName = getModelName(i);
            Path modelPath = fileSystem.makeQualified(new Path(super.getPathFinder().getModelsPath(sourceType), modelName));
            Path bModelPath = fileSystem.makeQualified(new Path(super.getPathFinder().getNNBinaryModelsPath(sourceType), modelName));
            // check if job is continuous training, this can be set multiple times and we only get last one
            boolean isContinuous = false;
            if (gs.hasHyperParam()) {
                isContinuous = false;
            } else {
                int intContinuous = checkContinuousTraining(fileSystem, localArgs, modelPath, modelConfig.getTrain().getParams());
                if (intContinuous == -1) {
                    LOG.warn("Model with index {} with size of trees is over treeNum, such training will not be started.", i);
                    continue;
                } else {
                    isContinuous = (intContinuous == 1);
                }
            }
            // training
            if (gs.hasHyperParam() || isKFoldCV) {
                isContinuous = false;
            }
            if (!isContinuous && !isOneJobNotContinuous) {
                isOneJobNotContinuous = true;
                // delete all old models if not continuous
                String srcModelPath = super.getPathFinder().getModelsPath(sourceType);
                String mvModelPath = srcModelPath + "_" + System.currentTimeMillis();
                LOG.info("Old model path has been moved to {}", mvModelPath);
                fileSystem.rename(new Path(srcModelPath), new Path(mvModelPath));
                fileSystem.mkdirs(new Path(srcModelPath));
                FileSystem.getLocal(conf).delete(new Path(super.getPathFinder().getModelsPath(SourceType.LOCAL)), true);
            }
            if (NNConstants.NN_ALG_NAME.equalsIgnoreCase(alg)) {
                // tree related parameters initialization
                Map<String, Object> params = gs.hasHyperParam() ? gs.getParams(i) : this.modelConfig.getTrain().getParams();
                Object fssObj = params.get("FeatureSubsetStrategy");
                FeatureSubsetStrategy featureSubsetStrategy = null;
                double featureSubsetRate = 0d;
                if (fssObj != null) {
                    try {
                        featureSubsetRate = Double.parseDouble(fssObj.toString());
                        // no need validate featureSubsetRate is in (0,1], as already validated in ModelInspector
                        featureSubsetStrategy = null;
                    } catch (NumberFormatException ee) {
                        featureSubsetStrategy = FeatureSubsetStrategy.of(fssObj.toString());
                    }
                } else {
                    LOG.warn("FeatureSubsetStrategy is not set, set to ALL by default.");
                    featureSubsetStrategy = FeatureSubsetStrategy.ALL;
                    featureSubsetRate = 0;
                }
                Set<Integer> subFeatures = null;
                if (isContinuous) {
                    BasicFloatNetwork existingModel = (BasicFloatNetwork) ModelSpecLoaderUtils.getBasicNetwork(ModelSpecLoaderUtils.loadModel(modelConfig, modelPath, ShifuFileUtils.getFileSystemBySourceType(this.modelConfig.getDataSet().getSource())));
                    if (existingModel == null) {
                        subFeatures = new HashSet<Integer>(getSubsamplingFeatures(allFeatures, featureSubsetStrategy, featureSubsetRate, inputNodeCount));
                    } else {
                        subFeatures = existingModel.getFeatureSet();
                    }
                } else {
                    subFeatures = new HashSet<Integer>(getSubsamplingFeatures(allFeatures, featureSubsetStrategy, featureSubsetRate, inputNodeCount));
                }
                if (subFeatures == null || subFeatures.size() == 0) {
                    localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.SHIFU_NN_FEATURE_SUBSET, ""));
                } else {
                    localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.SHIFU_NN_FEATURE_SUBSET, StringUtils.join(subFeatures, ',')));
                    LOG.debug("Size: {}, list: {}.", subFeatures.size(), StringUtils.join(subFeatures, ','));
                }
            }
            localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.GUAGUA_OUTPUT, modelPath.toString()));
            localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, Constants.SHIFU_NN_BINARY_MODEL_PATH, bModelPath.toString()));
            if (gs.hasHyperParam() || isKFoldCV) {
                // k-fold cv need val error
                Path valErrPath = fileSystem.makeQualified(new Path(super.getPathFinder().getValErrorPath(sourceType), "val_error_" + i));
                localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.GS_VALIDATION_ERROR, valErrPath.toString()));
            }
            localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.SHIFU_TRAINER_ID, String.valueOf(i)));
            final String progressLogFile = getProgressLogFile(i);
            progressLogList.add(progressLogFile);
            localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, CommonConstants.SHIFU_DTRAIN_PROGRESS_FILE, progressLogFile));
            String hdpVersion = HDPUtils.getHdpVersionForHDP224();
            if (StringUtils.isNotBlank(hdpVersion)) {
                localArgs.add(String.format(CommonConstants.MAPREDUCE_PARAM_FORMAT, "hdp.version", hdpVersion));
                HDPUtils.addFileToClassPath(HDPUtils.findContainingFile("hdfs-site.xml"), conf);
                HDPUtils.addFileToClassPath(HDPUtils.findContainingFile("core-site.xml"), conf);
                HDPUtils.addFileToClassPath(HDPUtils.findContainingFile("mapred-site.xml"), conf);
                HDPUtils.addFileToClassPath(HDPUtils.findContainingFile("yarn-site.xml"), conf);
            }
            if (isParallel) {
                guaguaClient.addJob(localArgs.toArray(new String[0]));
            } else {
                TailThread tailThread = startTailThread(new String[] { progressLogFile });
                boolean ret = guaguaClient.createJob(localArgs.toArray(new String[0])).waitForCompletion(true);
                status += (ret ? 0 : 1);
                stopTailThread(tailThread);
            }
        }
        if (isParallel) {
            TailThread tailThread = startTailThread(progressLogList.toArray(new String[0]));
            status += guaguaClient.run();
            stopTailThread(tailThread);
        }
    }
    if (isKFoldCV) {
        // k-fold we also copy model files at last, such models can be used for evaluation
        for (int i = 0; i < baggingNum; i++) {
            String modelName = getModelName(i);
            Path modelPath = fileSystem.makeQualified(new Path(super.getPathFinder().getModelsPath(sourceType), modelName));
            if (ShifuFileUtils.getFileSystemBySourceType(sourceType).exists(modelPath)) {
                copyModelToLocal(modelName, modelPath, sourceType);
            } else {
                LOG.warn("Model {} isn't there, maybe job is failed, for bagging it can be ignored.", modelPath.toString());
                status += 1;
            }
        }
        List<Double> valErrs = readAllValidationErrors(sourceType, fileSystem, kCrossValidation);
        double sum = 0d;
        for (Double err : valErrs) {
            sum += err;
        }
        LOG.info("Average validation error for current k-fold cross validation is {}.", sum / valErrs.size());
        LOG.info("K-fold cross validation on distributed training finished in {}ms.", System.currentTimeMillis() - start);
    } else if (gs.hasHyperParam()) {
        // select the best parameter composite in grid search
        LOG.info("Original grid search params: {}", modelConfig.getParams());
        Map<String, Object> params = findBestParams(sourceType, fileSystem, gs);
        // temp copy all models for evaluation
        for (int i = 0; i < baggingNum; i++) {
            String modelName = getModelName(i);
            Path modelPath = fileSystem.makeQualified(new Path(super.getPathFinder().getModelsPath(sourceType), modelName));
            if (ShifuFileUtils.getFileSystemBySourceType(sourceType).exists(modelPath) && (status == 0)) {
                copyModelToLocal(modelName, modelPath, sourceType);
            } else {
                LOG.warn("Model {} isn't there, maybe job is failed, for bagging it can be ignored.", modelPath.toString());
            }
        }
        LOG.info("The best parameters in grid search is {}", params);
        LOG.info("Grid search on distributed training finished in {}ms.", System.currentTimeMillis() - start);
    } else {
        // copy model files at last.
        for (int i = 0; i < baggingNum; i++) {
            String modelName = getModelName(i);
            Path modelPath = fileSystem.makeQualified(new Path(super.getPathFinder().getModelsPath(sourceType), modelName));
            if (ShifuFileUtils.getFileSystemBySourceType(sourceType).exists(modelPath) && (status == 0)) {
                copyModelToLocal(modelName, modelPath, sourceType);
            } else {
                LOG.warn("Model {} isn't there, maybe job is failed, for bagging it can be ignored.", modelPath.toString());
            }
        }
        // copy temp model files, for RF/GBT, not to copy tmp models because of larger space needed, for others
        // by default copy tmp models to local
        boolean copyTmpModelsToLocal = Boolean.TRUE.toString().equalsIgnoreCase(Environment.getProperty(Constants.SHIFU_TMPMODEL_COPYTOLOCAL, "true"));
        if (copyTmpModelsToLocal) {
            copyTmpModelsToLocal(tmpModelsPath, sourceType);
        } else {
            LOG.info("Tmp models are not copied into local, please find them in hdfs path: {}", tmpModelsPath);
        }
        LOG.info("Distributed training finished in {}ms.", System.currentTimeMillis() - start);
    }
    if (CommonUtils.isTreeModel(modelConfig.getAlgorithm())) {
        List<BasicML> models = ModelSpecLoaderUtils.loadBasicModels(this.modelConfig, null);
        // compute feature importance and write to local file after models are trained
        Map<Integer, MutablePair<String, Double>> featureImportances = CommonUtils.computeTreeModelFeatureImportance(models);
        String localFsFolder = pathFinder.getLocalFeatureImportanceFolder();
        String localFIPath = pathFinder.getLocalFeatureImportancePath();
        processRollupForFIFiles(localFsFolder, localFIPath);
        CommonUtils.writeFeatureImportance(localFIPath, featureImportances);
    }
    if (status != 0) {
        LOG.error("Error may occurred. There is no model generated. Please check!");
    }
    return status;
}

Example 4 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class ModelStatsCreator method build.

@Override
public ModelStats build(BasicML basicML) {
    ModelStats modelStats = new ModelStats();
    if (basicML instanceof BasicFloatNetwork) {
        BasicFloatNetwork bfn = (BasicFloatNetwork) basicML;
        Set<Integer> featureSet = bfn.getFeatureSet();
        for (ColumnConfig columnConfig : columnConfigList) {
            if (columnConfig.isFinalSelect() && (CollectionUtils.isEmpty(featureSet) || featureSet.contains(columnConfig.getColumnNum()))) {
                UnivariateStats univariateStats = new UnivariateStats();
                // here, no need to consider if column is in segment expansion
                // as we need to address new stats variable
                // set simple column name in PMML
                univariateStats.setField(FieldName.create(NormalUtils.getSimpleColumnName(columnConfig.getColumnName())));
                if (columnConfig.isCategorical()) {
                    DiscrStats discrStats = new DiscrStats();
                    Array countArray = createCountArray(columnConfig);
                    discrStats.addArrays(countArray);
                    if (!isConcise) {
                        List<Extension> extensions = createExtensions(columnConfig);
                        discrStats.addExtensions(extensions.toArray(new Extension[extensions.size()]));
                    }
                    univariateStats.setDiscrStats(discrStats);
                } else {
                    // numerical column
                    univariateStats.setNumericInfo(createNumericInfo(columnConfig));
                    if (!isConcise) {
                        univariateStats.setContStats(createConStats(columnConfig));
                    }
                }
                modelStats.addUnivariateStats(univariateStats);
            }
        }
    } else {
        for (ColumnConfig columnConfig : columnConfigList) {
            if (columnConfig.isFinalSelect()) {
                UnivariateStats univariateStats = new UnivariateStats();
                // here, no need to consider if column is in segment expansion as we need to address new stats
                // variable
                univariateStats.setField(FieldName.create(NormalUtils.getSimpleColumnName(columnConfig.getColumnName())));
                if (columnConfig.isCategorical()) {
                    DiscrStats discrStats = new DiscrStats();
                    Array countArray = createCountArray(columnConfig);
                    discrStats.addArrays(countArray);
                    if (!isConcise) {
                        List<Extension> extensions = createExtensions(columnConfig);
                        discrStats.addExtensions(extensions.toArray(new Extension[extensions.size()]));
                    }
                    univariateStats.setDiscrStats(discrStats);
                } else {
                    // numerical column
                    univariateStats.setNumericInfo(createNumericInfo(columnConfig));
                    if (!isConcise) {
                        univariateStats.setContStats(createConStats(columnConfig));
                    }
                }
                modelStats.addUnivariateStats(univariateStats);
            }
        }
    }
    return modelStats;
}

Example 5 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class DataDictionaryCreator method build.

@Override
public DataDictionary build(BasicML basicML) {
    DataDictionary dict = new DataDictionary();
    List<DataField> fields = new ArrayList<DataField>();
    boolean isSegExpansionMode = columnConfigList.size() > datasetHeaders.length;
    int segSize = segmentExpansions.size();
    if (basicML != null && basicML instanceof BasicFloatNetwork) {
        BasicFloatNetwork bfn = (BasicFloatNetwork) basicML;
        Set<Integer> featureSet = bfn.getFeatureSet();
        for (ColumnConfig columnConfig : columnConfigList) {
            if (columnConfig.getColumnNum() >= datasetHeaders.length) {
                // in order
                break;
            }
            if (isConcise) {
                if (columnConfig.isFinalSelect() && (CollectionUtils.isEmpty(featureSet) || featureSet.contains(columnConfig.getColumnNum())) || columnConfig.isTarget()) {
                    fields.add(convertColumnToDataField(columnConfig));
                } else if (isSegExpansionMode) {
                    // even current column not selected, if segment column selected, we should keep raw column
                    for (int i = 0; i < segSize; i++) {
                        int newIndex = datasetHeaders.length * (i + 1) + columnConfig.getColumnNum();
                        ColumnConfig cc = columnConfigList.get(newIndex);
                        if (cc.isFinalSelect()) {
                            // if one segment feature is selected, we should put raw column in
                            fields.add(convertColumnToDataField(columnConfig));
                            break;
                        }
                    }
                }
            } else {
                fields.add(convertColumnToDataField(columnConfig));
            }
        }
    } else {
        for (ColumnConfig columnConfig : columnConfigList) {
            if (columnConfig.getColumnNum() >= datasetHeaders.length) {
                // in order
                break;
            }
            if (isConcise) {
                if (columnConfig.isFinalSelect() || columnConfig.isTarget()) {
                    fields.add(convertColumnToDataField(columnConfig));
                } else if (isSegExpansionMode) {
                    // even current column not selected, if segment column selected, we should keep raw column
                    for (int i = 0; i < segSize; i++) {
                        int newIndex = datasetHeaders.length * (i + 1) + columnConfig.getColumnNum();
                        ColumnConfig cc = columnConfigList.get(newIndex);
                        if (cc.isFinalSelect()) {
                            // if one segment feature is selected, we should put raw column in
                            fields.add(convertColumnToDataField(columnConfig));
                            break;
                        }
                    }
                }
            } else {
                fields.add(convertColumnToDataField(columnConfig));
            }
        }
    }
    dict.addDataFields(fields.toArray(new DataField[fields.size()]));
    dict.setNumberOfFields(fields.size());
    return dict;
}