Examples with GridSearch ml.shifu.shifu.core.dtrain.gs.GridSearch used on opensource projects

Example 11 with GridSearch

use of ml.shifu.shifu.core.dtrain.gs.GridSearch in project shifu by ShifuML.

the class MetaFactory method validate.

/**
 * Validate the ModelConfig object, to make sure each item follow the constrain
 *
 * @param modelConfig
 *            - object to validate
 * @return ValidateResult
 *         If all items are OK, the ValidateResult.status will be true;
 *         Or the ValidateResult.status will be false, ValidateResult.causes will contain the reasons
 * @throws Exception
 *             any exception in validaiton
 */
public static ValidateResult validate(ModelConfig modelConfig) throws Exception {
    ValidateResult result = new ValidateResult(true);
    GridSearch gs = new GridSearch(modelConfig.getTrain().getParams(), modelConfig.getTrain().getGridConfigFileContent());
    Class<?> cls = modelConfig.getClass();
    Field[] fields = cls.getDeclaredFields();
    for (Field field : fields) {
        // skip log instance
        if (field.getName().equalsIgnoreCase("log") || field.getName().equalsIgnoreCase("logger")) {
            continue;
        }
        if (!field.isSynthetic()) {
            Method method = cls.getMethod("get" + getMethodName(field.getName()));
            Object value = method.invoke(modelConfig);
            if (value instanceof List) {
                List<?> objList = (List<?>) value;
                for (Object obj : objList) {
                    encapsulateResult(result, iterateCheck(gs.hasHyperParam(), field.getName(), obj));
                }
            } else {
                encapsulateResult(result, iterateCheck(gs.hasHyperParam(), field.getName(), value));
            }
        }
    }
    return result;
}

Example 12 with GridSearch

use of ml.shifu.shifu.core.dtrain.gs.GridSearch in project shifu by ShifuML.

the class AbstractNNWorker method init.

@Override
public void init(WorkerContext<NNParams, NNParams> context) {
    // load props firstly
    this.props = context.getProps();
    loadConfigFiles(context.getProps());
    this.trainerId = Integer.valueOf(context.getProps().getProperty(CommonConstants.SHIFU_TRAINER_ID, "0"));
    GridSearch gs = new GridSearch(modelConfig.getTrain().getParams(), modelConfig.getTrain().getGridConfigFileContent());
    this.validParams = this.modelConfig.getTrain().getParams();
    if (gs.hasHyperParam()) {
        this.validParams = gs.getParams(trainerId);
        LOG.info("Start grid search master with params: {}", validParams);
    }
    Integer kCrossValidation = this.modelConfig.getTrain().getNumKFold();
    if (kCrossValidation != null && kCrossValidation > 0) {
        isKFoldCV = true;
        LOG.info("Cross validation is enabled by kCrossValidation: {}.", kCrossValidation);
    }
    this.poissonSampler = Boolean.TRUE.toString().equalsIgnoreCase(context.getProps().getProperty(NNConstants.NN_POISON_SAMPLER));
    this.rng = new PoissonDistribution(1.0d);
    Double upSampleWeight = modelConfig.getTrain().getUpSampleWeight();
    if (Double.compare(upSampleWeight, 1d) != 0 && (modelConfig.isRegression() || (modelConfig.isClassification() && modelConfig.getTrain().isOneVsAll()))) {
        // set mean to upSampleWeight -1 and get sample + 1to make sure no zero sample value
        LOG.info("Enable up sampling with weight {}.", upSampleWeight);
        this.upSampleRng = new PoissonDistribution(upSampleWeight - 1);
    }
    Integer epochsPerIterationInteger = this.modelConfig.getTrain().getEpochsPerIteration();
    this.epochsPerIteration = epochsPerIterationInteger == null ? 1 : epochsPerIterationInteger.intValue();
    LOG.info("epochsPerIteration in worker is :{}", epochsPerIteration);
    // Object elmObject = validParams.get(DTrainUtils.IS_ELM);
    // isELM = elmObject == null ? false : "true".equalsIgnoreCase(elmObject.toString());
    // LOG.info("Check isELM: {}", isELM);
    Object dropoutRateObj = validParams.get(CommonConstants.DROPOUT_RATE);
    if (dropoutRateObj != null) {
        this.dropoutRate = Double.valueOf(dropoutRateObj.toString());
    }
    LOG.info("'dropoutRate' in worker is :{}", this.dropoutRate);
    Object miniBatchO = validParams.get(CommonConstants.MINI_BATCH);
    if (miniBatchO != null) {
        int miniBatchs;
        try {
            miniBatchs = Integer.parseInt(miniBatchO.toString());
        } catch (Exception e) {
            miniBatchs = 1;
        }
        if (miniBatchs < 0) {
            this.batchs = 1;
        } else if (miniBatchs > 1000) {
            this.batchs = 1000;
        } else {
            this.batchs = miniBatchs;
        }
        LOG.info("'miniBatchs' in worker is : {}, batchs is {} ", miniBatchs, batchs);
    }
    int[] inputOutputIndex = DTrainUtils.getInputOutputCandidateCounts(modelConfig.getNormalizeType(), this.columnConfigList);
    this.inputNodeCount = inputOutputIndex[0] == 0 ? inputOutputIndex[2] : inputOutputIndex[0];
    // if is one vs all classification, outputNodeCount is set to 1, if classes=2, outputNodeCount is also 1
    int classes = modelConfig.getTags().size();
    this.outputNodeCount = (isLinearTarget || modelConfig.isRegression()) ? inputOutputIndex[1] : (modelConfig.getTrain().isOneVsAll() ? inputOutputIndex[1] : (classes == 2 ? 1 : classes));
    this.candidateCount = inputOutputIndex[2];
    boolean isAfterVarSelect = inputOutputIndex[0] != 0;
    LOG.info("isAfterVarSelect {}: Input count {}, output count {}, candidate count {}", isAfterVarSelect, inputNodeCount, outputNodeCount, candidateCount);
    // cache all feature list for sampling features
    this.allFeatures = NormalUtils.getAllFeatureList(columnConfigList, isAfterVarSelect);
    String subsetStr = context.getProps().getProperty(CommonConstants.SHIFU_NN_FEATURE_SUBSET);
    if (StringUtils.isBlank(subsetStr)) {
        this.subFeatures = this.allFeatures;
    } else {
        String[] splits = subsetStr.split(",");
        this.subFeatures = new ArrayList<Integer>(splits.length);
        for (String split : splits) {
            int featureIndex = Integer.parseInt(split);
            this.subFeatures.add(featureIndex);
        }
    }
    this.subFeatureSet = new HashSet<Integer>(this.subFeatures);
    LOG.info("subFeatures size is {}", subFeatures.size());
    this.featureInputsCnt = DTrainUtils.getFeatureInputsCnt(this.modelConfig, this.columnConfigList, this.subFeatureSet);
    this.wgtInit = "default";
    Object wgtInitObj = validParams.get(CommonConstants.WEIGHT_INITIALIZER);
    if (wgtInitObj != null) {
        this.wgtInit = wgtInitObj.toString();
    }
    Object lossObj = validParams.get("Loss");
    this.lossStr = lossObj != null ? lossObj.toString() : "squared";
    LOG.info("Loss str is {}", this.lossStr);
    this.isDry = Boolean.TRUE.toString().equalsIgnoreCase(context.getProps().getProperty(CommonConstants.SHIFU_DRY_DTRAIN));
    this.isSpecificValidation = (modelConfig.getValidationDataSetRawPath() != null && !"".equals(modelConfig.getValidationDataSetRawPath()));
    this.isStratifiedSampling = this.modelConfig.getTrain().getStratifiedSample();
    if (isOnDisk()) {
        LOG.info("NNWorker is loading data into disk.");
        try {
            initDiskDataSet();
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
        // cannot find a good place to close these two data set, using Shutdown hook
        Runtime.getRuntime().addShutdownHook(new Thread(new Runnable() {

            @Override
            public void run() {
                ((BufferedFloatMLDataSet) (AbstractNNWorker.this.trainingData)).close();
                ((BufferedFloatMLDataSet) (AbstractNNWorker.this.validationData)).close();
            }
        }));
    } else {
        LOG.info("NNWorker is loading data into memory.");
        double memoryFraction = Double.valueOf(context.getProps().getProperty("guagua.data.memoryFraction", "0.6"));
        long memoryStoreSize = (long) (Runtime.getRuntime().maxMemory() * memoryFraction);
        LOG.info("Max heap memory: {}, fraction: {}", Runtime.getRuntime().maxMemory(), memoryFraction);
        double crossValidationRate = this.modelConfig.getValidSetRate();
        try {
            if (StringUtils.isNotBlank(modelConfig.getValidationDataSetRawPath())) {
                // fixed 0.6 and 0.4 of max memory for trainingData and validationData
                this.trainingData = new MemoryDiskFloatMLDataSet((long) (memoryStoreSize * 0.6), DTrainUtils.getTrainingFile().toString(), this.featureInputsCnt, this.outputNodeCount);
                this.validationData = new MemoryDiskFloatMLDataSet((long) (memoryStoreSize * 0.4), DTrainUtils.getTestingFile().toString(), this.featureInputsCnt, this.outputNodeCount);
            } else {
                this.trainingData = new MemoryDiskFloatMLDataSet((long) (memoryStoreSize * (1 - crossValidationRate)), DTrainUtils.getTrainingFile().toString(), this.featureInputsCnt, this.outputNodeCount);
                this.validationData = new MemoryDiskFloatMLDataSet((long) (memoryStoreSize * crossValidationRate), DTrainUtils.getTestingFile().toString(), this.featureInputsCnt, this.outputNodeCount);
            }
            // cannot find a good place to close these two data set, using Shutdown hook
            Runtime.getRuntime().addShutdownHook(new Thread(new Runnable() {

                @Override
                public void run() {
                    ((MemoryDiskFloatMLDataSet) (AbstractNNWorker.this.trainingData)).close();
                    ((MemoryDiskFloatMLDataSet) (AbstractNNWorker.this.validationData)).close();
                }
            }));
        } catch (IOException e) {
            throw new GuaguaRuntimeException(e);
        }
    }
    // create Splitter
    String delimiter = context.getProps().getProperty(Constants.SHIFU_OUTPUT_DATA_DELIMITER);
    this.splitter = MapReduceUtils.generateShifuOutputSplitter(delimiter);
}

Example 13 with GridSearch

use of ml.shifu.shifu.core.dtrain.gs.GridSearch in project shifu by ShifuML.

the class NNMaster method init.

@SuppressWarnings("unchecked")
@Override
public void init(MasterContext<NNParams, NNParams> context) {
    Properties props = context.getProps();
    try {
        SourceType sourceType = SourceType.valueOf(props.getProperty(CommonConstants.MODELSET_SOURCE_TYPE, SourceType.HDFS.toString()));
        this.modelConfig = CommonUtils.loadModelConfig(props.getProperty(CommonConstants.SHIFU_MODEL_CONFIG), sourceType);
        this.columnConfigList = CommonUtils.loadColumnConfigList(props.getProperty(CommonConstants.SHIFU_COLUMN_CONFIG), sourceType);
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    int trainerId = Integer.valueOf(context.getProps().getProperty(CommonConstants.SHIFU_TRAINER_ID, "0"));
    GridSearch gs = new GridSearch(modelConfig.getTrain().getParams(), modelConfig.getTrain().getGridConfigFileContent());
    validParams = this.modelConfig.getTrain().getParams();
    if (gs.hasHyperParam()) {
        validParams = gs.getParams(trainerId);
        LOG.info("Start grid search master with params: {}", validParams);
    }
    Boolean enabledEarlyStop = DTrainUtils.getBoolean(validParams, CommonConstants.ENABLE_EARLY_STOP, Boolean.FALSE);
    if (enabledEarlyStop) {
        Double validTolerance = DTrainUtils.getDouble(validParams, CommonConstants.VALIDATION_TOLERANCE, null);
        if (validTolerance == null) {
            LOG.info("Early Stop is enabled. use WindowEarlyStop");
            // windowSize default 20, user should could adjust it
            this.earlyStopStrategy = new WindowEarlyStop(context, this.modelConfig, DTrainUtils.getInt(context.getProps(), CommonConstants.SHIFU_TRAIN_EARLYSTOP_WINDOW_SIZE, 20));
        } else {
            LOG.info("Early Stop is enabled. use ConvergeAndValiToleranceEarlyStop");
            Double threshold = this.modelConfig.getTrain().getConvergenceThreshold();
            this.earlyStopStrategy = new ConvergeAndValidToleranceEarlyStop(threshold == null ? Double.MIN_VALUE : threshold.doubleValue(), validTolerance);
        }
    }
    Object pObject = validParams.get(CommonConstants.PROPAGATION);
    this.propagation = pObject == null ? "Q" : (String) pObject;
    this.rawLearningRate = Double.valueOf(validParams.get(CommonConstants.LEARNING_RATE).toString());
    Object dropoutRateObj = validParams.get(CommonConstants.DROPOUT_RATE);
    if (dropoutRateObj != null) {
        this.dropoutRate = Double.valueOf(dropoutRateObj.toString());
    }
    LOG.info("'dropoutRate' in master is : {}", this.dropoutRate);
    Object learningDecayO = validParams.get(CommonConstants.LEARNING_DECAY);
    if (learningDecayO != null) {
        this.learningDecay = Double.valueOf(learningDecayO.toString());
    }
    LOG.info("'learningDecay' in master is :{}", learningDecay);
    Object momentumO = validParams.get("Momentum");
    if (momentumO != null) {
        this.momentum = Double.valueOf(momentumO.toString());
    }
    LOG.info("'momentum' in master is :{}", momentum);
    Object adamBeta1O = validParams.get("AdamBeta1");
    if (adamBeta1O != null) {
        this.adamBeta1 = Double.valueOf(adamBeta1O.toString());
    }
    LOG.info("'adamBeta1' in master is :{}", adamBeta1);
    Object adamBeta2O = validParams.get("AdamBeta2");
    if (adamBeta2O != null) {
        this.adamBeta2 = Double.valueOf(adamBeta2O.toString());
    }
    LOG.info("'adamBeta2' in master is :{}", adamBeta2);
    this.wgtInit = "default";
    Object wgtInitObj = validParams.get(CommonConstants.WEIGHT_INITIALIZER);
    if (wgtInitObj != null) {
        this.wgtInit = wgtInitObj.toString();
    }
    this.isContinuousEnabled = Boolean.TRUE.toString().equalsIgnoreCase(context.getProps().getProperty(CommonConstants.CONTINUOUS_TRAINING));
    Object rconstant = validParams.get(CommonConstants.REGULARIZED_CONSTANT);
    this.regularizedConstant = NumberFormatUtils.getDouble(rconstant == null ? "" : rconstant.toString(), 0d);
    // We do not update weight in fixed layers so that we could fine tune other layers of NN
    Object fixedLayers2O = validParams.get(CommonConstants.FIXED_LAYERS);
    if (fixedLayers2O != null) {
        this.fixedLayers = (List<Integer>) fixedLayers2O;
    }
    LOG.info("Fixed layers in master is :{}", this.fixedLayers.toString());
    Object fixedBiasObj = validParams.getOrDefault(CommonConstants.FIXED_BIAS, true);
    this.fixedBias = (Boolean) fixedBiasObj;
    Object hiddenLayerNumObj = validParams.get(CommonConstants.NUM_HIDDEN_LAYERS);
    if (hiddenLayerNumObj != null && StringUtils.isNumeric(hiddenLayerNumObj.toString())) {
        this.hiddenLayerNum = Integer.valueOf(hiddenLayerNumObj.toString());
    }
    LOG.info("hiddenLayerNum in master is :{}", this.hiddenLayerNum);
    // check if variables are set final selected
    int[] inputOutputIndex = DTrainUtils.getNumericAndCategoricalInputAndOutputCounts(this.columnConfigList);
    this.isAfterVarSelect = (inputOutputIndex[3] == 1);
    // cache all feature list for sampling features
    this.allFeatures = NormalUtils.getAllFeatureList(columnConfigList, isAfterVarSelect);
    String subsetStr = context.getProps().getProperty(CommonConstants.SHIFU_NN_FEATURE_SUBSET);
    if (StringUtils.isBlank(subsetStr)) {
        this.subFeatures = this.allFeatures;
    } else {
        String[] splits = subsetStr.split(",");
        this.subFeatures = new ArrayList<Integer>(splits.length);
        for (String split : splits) {
            this.subFeatures.add(Integer.parseInt(split));
        }
    }
    // not init but not first iteration, first recover from last master result set from guagua
    if (!context.isFirstIteration()) {
        NNParams params = context.getMasterResult();
        if (params != null && params.getWeights() != null) {
            this.globalNNParams.setWeights(params.getWeights());
        } else {
            // else read from checkpoint
            params = initOrRecoverParams(context);
            this.globalNNParams.setWeights(params.getWeights());
        }
    }
}

Example 14 with GridSearch

use of ml.shifu.shifu.core.dtrain.gs.GridSearch in project shifu by ShifuML.

the class LogisticRegressionMaster method init.

@Override
public void init(MasterContext<LogisticRegressionParams, LogisticRegressionParams> context) {
    loadConfigFiles(context.getProps());
    int trainerId = Integer.valueOf(context.getProps().getProperty(CommonConstants.SHIFU_TRAINER_ID, "0"));
    GridSearch gs = new GridSearch(modelConfig.getTrain().getParams(), modelConfig.getTrain().getGridConfigFileContent());
    validParams = this.modelConfig.getTrain().getParams();
    if (gs.hasHyperParam()) {
        validParams = gs.getParams(trainerId);
        LOG.info("Start grid search master with params: {}", validParams);
    }
    this.learningRate = Double.valueOf(this.validParams.get(CommonConstants.LEARNING_RATE).toString());
    int[] inputOutputIndex = DTrainUtils.getInputOutputCandidateCounts(modelConfig.getNormalizeType(), this.columnConfigList);
    this.inputNum = inputOutputIndex[0] == 0 ? inputOutputIndex[2] : inputOutputIndex[0];
    Boolean enabledEarlyStop = DTrainUtils.getBoolean(validParams, CommonConstants.ENABLE_EARLY_STOP, Boolean.FALSE);
    if (enabledEarlyStop) {
        Double validTolerance = DTrainUtils.getDouble(validParams, CommonConstants.VALIDATION_TOLERANCE, null);
        if (validTolerance == null) {
            LOG.info("Early Stop is enabled. use WindowEarlyStop");
            // windowSize default 20, user should could adjust it
            this.earlyStopStrategy = new WindowEarlyStop(context, this.modelConfig, DTrainUtils.getInt(context.getProps(), CommonConstants.SHIFU_TRAIN_EARLYSTOP_WINDOW_SIZE, 20));
        } else {
            LOG.info("Early Stop is enabled. use ConvergeAndValiToleranceEarlyStop");
            Double threshold = this.modelConfig.getTrain().getConvergenceThreshold();
            this.earlyStopStrategy = new ConvergeAndValidToleranceEarlyStop(threshold == null ? Double.MIN_VALUE : threshold.doubleValue(), validTolerance);
        }
    }
    Object pObject = validParams.get(CommonConstants.PROPAGATION);
    this.propagation = pObject == null ? "R" : (String) pObject;
    Object rconstant = validParams.get(CommonConstants.REGULARIZED_CONSTANT);
    this.regularizedConstant = NumberFormatUtils.getDouble(rconstant == null ? "" : rconstant.toString(), 0d);
    this.isContinuousEnabled = Boolean.TRUE.toString().equalsIgnoreCase(context.getProps().getProperty(CommonConstants.CONTINUOUS_TRAINING));
    LOG.info("continuousEnabled: {}", this.isContinuousEnabled);
    // not initialized and not first iteration, should be fault tolerance, recover state in LogisticRegressionMaster
    if (!context.isFirstIteration()) {
        LogisticRegressionParams lastMasterResult = context.getMasterResult();
        if (lastMasterResult != null && lastMasterResult.getParameters() != null) {
            // recover state in current master computable and return to workers
            this.weights = lastMasterResult.getParameters();
        } else {
            // no weights, restarted from the very beginning, this may not happen
            this.weights = initWeights().getParameters();
        }
    }
}